From c09594d7a84d88ad0ff998e0c0e2206ed3b3c191 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Tue, 2 Mar 2021 15:09:56 +0800
Subject: [PATCH 01/29] dice loss
---
mmseg/models/losses/__init__.py | 3 +-
mmseg/models/losses/dice_loss.py | 97 ++++++++++++++++++++++++++++++++
tests/test_models/test_losses.py | 40 +++++++++++++
3 files changed, 139 insertions(+), 1 deletion(-)
create mode 100644 mmseg/models/losses/dice_loss.py
diff --git a/mmseg/models/losses/__init__.py b/mmseg/models/losses/__init__.py
index d623887760..4401bd42d0 100644
--- a/mmseg/models/losses/__init__.py
+++ b/mmseg/models/losses/__init__.py
@@ -2,10 +2,11 @@
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .lovasz_loss import LovaszLoss
+from .dice_loss import DiceLoss
from .utils import reduce_loss, weight_reduce_loss, weighted_loss
__all__ = [
'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
- 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss'
+ 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss'
]
diff --git a/mmseg/models/losses/dice_loss.py b/mmseg/models/losses/dice_loss.py
new file mode 100644
index 0000000000..0956d26f9f
--- /dev/null
+++ b/mmseg/models/losses/dice_loss.py
@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+@weighted_loss
+def dice_loss(pred,
+ target,
+ valid_mask,
+ smooth=1,
+ exponent=2,
+ class_weight=None,
+ ignore_index=-1):
+ assert pred.shape[0] == target.shape[0]
+ total_loss = 0
+ num_classes = pred.shape[1]
+ for i in range(num_classes):
+ if i != ignore_index:
+ dice_loss = binary_dice_loss(pred[:, i], target[..., i], valid_mask=valid_mask, smooth=smooth, exponent=exponent)
+ if class_weight is not None:
+ dice_loss *= class_weight[i]
+ total_loss += dice_loss
+ return total_loss / num_classes
+
+@weighted_loss
+def binary_dice_loss(pred,
+ target,
+ valid_mask,
+ smooth=1,
+ exponent=2,
+ **kwards):
+ assert pred.shape[0] == target.shape[0]
+ pred = pred.contiguous().view(pred.shape[0], -1)
+ target = target.contiguous().view(target.shape[0], -1)
+ valid_mask = valid_mask.contiguous().view(valid_mask.shape[0], -1)
+
+ num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
+ den = torch.sum((pred.pow(exponent) + target.pow(exponent)) * valid_mask, dim=1) + smooth
+
+ return 1 - num / den
+
+@LOSSES.register_module()
+class DiceLoss(nn.Module):
+ """DiceLoss.
+
+ """
+ def __init__(self,
+ loss_type='multi_class',
+ smooth=1,
+ exponent=2,
+ reduction='mean',
+ class_weight=None,
+ loss_weight=1.0,
+ ignore_index=-1):
+ super(DiceLoss, self).__init__()
+ assert loss_type in ['multi_class', 'binary']
+ if loss_type == 'multi_class':
+ self.cls_criterion = dice_loss
+ else:
+ self.cls_criterion = binary_dice_loss
+ self.smooth = smooth
+ self.exponent = exponent
+ self.reduction = reduction
+ self.class_weight = class_weight
+ self.loss_weight = loss_weight
+ self.ignore_index = ignore_index
+
+ def forward(self,
+ pred,
+ target,
+ avg_factor=None,
+ reduction_override=None):
+ assert reduction_override in (None, 'none', 'mean', 'sum')
+ reduction = (
+ reduction_override if reduction_override else self.reduction)
+ if self.class_weight is not None:
+ class_weight = pred.new_tensor(self.class_weight)
+ else:
+ class_weight = None
+
+ pred = F.softmax(pred, dim=1)
+ one_hot_target = F.one_hot(torch.clamp_min(target.long(), 0))
+ valid_mask = (target != self.ignore_index).long()
+
+ loss = self.loss_weight * self.cls_criterion(
+ pred,
+ one_hot_target,
+ valid_mask=valid_mask,
+ reduction=reduction,
+ avg_factor=avg_factor,
+ smooth=self.smooth,
+ exponent=self.exponent,
+ class_weight=class_weight,
+ ignore_index=self.ignore_index)
+ return loss
diff --git a/tests/test_models/test_losses.py b/tests/test_models/test_losses.py
index 005d939114..0984003bdc 100644
--- a/tests/test_models/test_losses.py
+++ b/tests/test_models/test_losses.py
@@ -202,3 +202,43 @@ def test_lovasz_loss():
logits = torch.rand(2, 4, 4)
labels = (torch.rand(2, 4, 4)).long()
lovasz_loss(logits, labels, ignore_index=None)
+
+def test_dice_lose():
+ from mmseg.models import build_loss
+ import sys
+
+ # loss_type should be 'binary' or 'multi_class'
+ with pytest.raises(AssertionError):
+ loss_cfg = dict(
+ type='DiceLoss',
+ loss_type='Binary',
+ reduction='none',
+ loss_weight=1.0)
+ build_loss(loss_cfg)
+
+ # test dice loss with loss_type = 'multi_class'
+ loss_cfg = dict(
+ type='DiceLoss',
+ loss_type='multi_class',
+ reduction='none',
+ class_weight=[1.0, 2.0, 3.0],
+ loss_weight=1.0,
+ ignore_index=1)
+ dice_loss = build_loss(loss_cfg)
+ logits = torch.rand(8, 3, 4, 4)
+ labels = (torch.rand(8, 4, 4) * 3).long()
+ dice_loss(logits, labels)
+
+ # test dice loss with loss_type = 'binary'
+ loss_cfg = dict(
+ type='DiceLoss',
+ loss_type='binary',
+ smooth=2,
+ exponent=3,
+ reduction='sum',
+ loss_weight=1.0,
+ ignore_index=0)
+ dice_loss = build_loss(loss_cfg)
+ logits = torch.rand(16, 4, 4)
+ labels = (torch.rand(16, 4, 4)).long()
+ dice_loss(logits, labels)
\ No newline at end of file
From 85d5eb4c940936568cc283ee099e44bd7f787bd2 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Tue, 2 Mar 2021 20:35:43 +0800
Subject: [PATCH 02/29] format code, add docstring and calculate denominator
without valid_mask
---
mmseg/models/losses/dice_loss.py | 67 ++++++++++++++++++++------------
tests/test_models/test_losses.py | 6 +--
2 files changed, 45 insertions(+), 28 deletions(-)
diff --git a/mmseg/models/losses/dice_loss.py b/mmseg/models/losses/dice_loss.py
index 0956d26f9f..06d44a9497 100644
--- a/mmseg/models/losses/dice_loss.py
+++ b/mmseg/models/losses/dice_loss.py
@@ -5,11 +5,12 @@
from ..builder import LOSSES
from .utils import weighted_loss
+
@weighted_loss
def dice_loss(pred,
target,
valid_mask,
- smooth=1,
+ smooth=1,
exponent=2,
class_weight=None,
ignore_index=-1):
@@ -18,40 +19,60 @@ def dice_loss(pred,
num_classes = pred.shape[1]
for i in range(num_classes):
if i != ignore_index:
- dice_loss = binary_dice_loss(pred[:, i], target[..., i], valid_mask=valid_mask, smooth=smooth, exponent=exponent)
+ dice_loss = binary_dice_loss(
+ pred[:, i],
+ target[..., i],
+ valid_mask=valid_mask,
+ smooth=smooth,
+ exponent=exponent)
if class_weight is not None:
dice_loss *= class_weight[i]
total_loss += dice_loss
return total_loss / num_classes
+
@weighted_loss
-def binary_dice_loss(pred,
- target,
- valid_mask,
- smooth=1,
- exponent=2,
- **kwards):
+def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
assert pred.shape[0] == target.shape[0]
pred = pred.contiguous().view(pred.shape[0], -1)
target = target.contiguous().view(target.shape[0], -1)
valid_mask = valid_mask.contiguous().view(valid_mask.shape[0], -1)
num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
- den = torch.sum((pred.pow(exponent) + target.pow(exponent)) * valid_mask, dim=1) + smooth
+ den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
return 1 - num / den
+
@LOSSES.register_module()
class DiceLoss(nn.Module):
"""DiceLoss.
+ This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
+ Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.
+
+ Args:
+ loss_type (str, optional): Binary or multi-class loss.
+ Default: 'multi_class'. Options are "binary" and "multi_class".
+ smooth (float): A float number to smooth loss, and avoid NaN error.
+ Default: 1
+ exponent (float): An float number to calculate denominator
+ value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
+ reduction (str, optional): The method used to reduce the loss. Options
+ are "none", "mean" and "sum". This parameter only works when
+ per_image is True. Default: 'mean'.
+ class_weight (list[float], optional): The weight for each class.
+ Default: None.
+ loss_weight (float, optional): Weight of the loss. Default to 1.0.
+ ignore_index (int | None): The label index to be ignored. Default: -1.
"""
- def __init__(self,
+
+ def __init__(self,
loss_type='multi_class',
- smooth=1,
+ smooth=1,
exponent=2,
reduction='mean',
- class_weight=None,
+ class_weight=None,
loss_weight=1.0,
ignore_index=-1):
super(DiceLoss, self).__init__()
@@ -67,29 +88,25 @@ def __init__(self,
self.loss_weight = loss_weight
self.ignore_index = ignore_index
- def forward(self,
- pred,
- target,
- avg_factor=None,
- reduction_override=None):
+ def forward(self, pred, target, avg_factor=None, reduction_override=None):
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.class_weight is not None:
class_weight = pred.new_tensor(self.class_weight)
else:
- class_weight = None
-
+ class_weight = None
+
pred = F.softmax(pred, dim=1)
- one_hot_target = F.one_hot(torch.clamp_min(target.long(), 0))
+ one_hot_target = F.one_hot(torch.clamp_min(target.long(), 0))
valid_mask = (target != self.ignore_index).long()
-
+
loss = self.loss_weight * self.cls_criterion(
- pred,
- one_hot_target,
+ pred,
+ one_hot_target,
valid_mask=valid_mask,
- reduction=reduction,
- avg_factor=avg_factor,
+ reduction=reduction,
+ avg_factor=avg_factor,
smooth=self.smooth,
exponent=self.exponent,
class_weight=class_weight,
diff --git a/tests/test_models/test_losses.py b/tests/test_models/test_losses.py
index 0984003bdc..481a8e92ce 100644
--- a/tests/test_models/test_losses.py
+++ b/tests/test_models/test_losses.py
@@ -203,9 +203,9 @@ def test_lovasz_loss():
labels = (torch.rand(2, 4, 4)).long()
lovasz_loss(logits, labels, ignore_index=None)
+
def test_dice_lose():
from mmseg.models import build_loss
- import sys
# loss_type should be 'binary' or 'multi_class'
with pytest.raises(AssertionError):
@@ -216,7 +216,7 @@ def test_dice_lose():
loss_weight=1.0)
build_loss(loss_cfg)
- # test dice loss with loss_type = 'multi_class'
+ # test dice loss with loss_type = 'multi_class'
loss_cfg = dict(
type='DiceLoss',
loss_type='multi_class',
@@ -241,4 +241,4 @@ def test_dice_lose():
dice_loss = build_loss(loss_cfg)
logits = torch.rand(16, 4, 4)
labels = (torch.rand(16, 4, 4)).long()
- dice_loss(logits, labels)
\ No newline at end of file
+ dice_loss(logits, labels)
From 631322bc1149b23a5fd80a8e457d2834b64d5993 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Tue, 2 Mar 2021 20:44:38 +0800
Subject: [PATCH 03/29] minor change
---
mmseg/models/losses/__init__.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/mmseg/models/losses/__init__.py b/mmseg/models/losses/__init__.py
index 4401bd42d0..beca720456 100644
--- a/mmseg/models/losses/__init__.py
+++ b/mmseg/models/losses/__init__.py
@@ -1,8 +1,8 @@
from .accuracy import Accuracy, accuracy
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
-from .lovasz_loss import LovaszLoss
from .dice_loss import DiceLoss
+from .lovasz_loss import LovaszLoss
from .utils import reduce_loss, weight_reduce_loss, weighted_loss
__all__ = [
From 20c9f627eaefa95acae95c59b1e329a5f6d7f464 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Thu, 11 Mar 2021 10:25:49 +0800
Subject: [PATCH 04/29] restore
---
mmseg/models/losses/dice_loss.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/mmseg/models/losses/dice_loss.py b/mmseg/models/losses/dice_loss.py
index 06d44a9497..27da861f98 100644
--- a/mmseg/models/losses/dice_loss.py
+++ b/mmseg/models/losses/dice_loss.py
@@ -1,3 +1,5 @@
+"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
+segmentron/solver/loss.py (Apache-2.0 License)"""
import torch
import torch.nn as nn
import torch.nn.functional as F
@@ -64,7 +66,7 @@ class DiceLoss(nn.Module):
class_weight (list[float], optional): The weight for each class.
Default: None.
loss_weight (float, optional): Weight of the loss. Default to 1.0.
- ignore_index (int | None): The label index to be ignored. Default: -1.
+ ignore_index (int | None): The label index to be ignored. Default: 255.
"""
def __init__(self,
@@ -74,7 +76,7 @@ def __init__(self,
reduction='mean',
class_weight=None,
loss_weight=1.0,
- ignore_index=-1):
+ ignore_index=255):
super(DiceLoss, self).__init__()
assert loss_type in ['multi_class', 'binary']
if loss_type == 'multi_class':
From bdbb9c684d589f4567091a7140664ed55362deca Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 14:40:00 +0800
Subject: [PATCH 05/29] add metafile
---
configs/fcn/metafile.yml | 7 +++++++
model_zoo.yml | 4 ++++
2 files changed, 11 insertions(+)
create mode 100644 configs/fcn/metafile.yml
create mode 100644 model_zoo.yml
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
new file mode 100644
index 0000000000..96ad802e9c
--- /dev/null
+++ b/configs/fcn/metafile.yml
@@ -0,0 +1,7 @@
+Collections:
+ - Name: FCN
+Models:
+ - Name: fcn_r50-d8_512x1024_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ Training Data: Cityscapes
diff --git a/model_zoo.yml b/model_zoo.yml
new file mode 100644
index 0000000000..aae808abb7
--- /dev/null
+++ b/model_zoo.yml
@@ -0,0 +1,4 @@
+Import:
+ - configs/fcn/metafile.yml
+ - configs/pspnet/metafile.yml
+ - configs/deeplabv3/metafile.yml
From 7a12e7c99d7b8c4e90fbb7a66e1d615fed03d0ca Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 15:06:45 +0800
Subject: [PATCH 06/29] add manifest.in and add config at setup.py
---
MANIFEST.in | 10 ++++++++++
setup.py | 1 +
2 files changed, 11 insertions(+)
create mode 100644 MANIFEST.in
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000000..f45525bccf
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,10 @@
+
+include requirements/*.txt
+include mmseg/VERSION
+include mmseg/model_zoo.yml
+include mmseg/configs/*/*.py
+include mmseg/configs/*/*.yml
+include mmseg/tools/*.py
+include mmseg/tools/*.sh
+include mmseg/tools/*/*.py
+include mmseg/demo/*/*
diff --git a/setup.py b/setup.py
index 2e69551b8f..321664bcdd 100755
--- a/setup.py
+++ b/setup.py
@@ -104,6 +104,7 @@ def gen_packages_items():
keywords='computer vision, semantic segmentation',
url='http://github.com/open-mmlab/mmsegmentation',
packages=find_packages(exclude=('configs', 'tools', 'demo')),
+ include_package_data=True,
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
From d11b00ee8d38a7636f1a759fcb5520788120cad4 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 15:12:06 +0800
Subject: [PATCH 07/29] add requirements
---
requirements/mminstall.txt | 1 +
1 file changed, 1 insertion(+)
create mode 100644 requirements/mminstall.txt
diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt
new file mode 100644
index 0000000000..d371e1cc8e
--- /dev/null
+++ b/requirements/mminstall.txt
@@ -0,0 +1 @@
+mmcv-full>=1.3.0
From 5a2448b8623c0c8f58b82ce14d6a79c93431b3a2 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 15:16:41 +0800
Subject: [PATCH 08/29] modify manifest
---
MANIFEST.in | 10 ++--------
1 file changed, 2 insertions(+), 8 deletions(-)
diff --git a/MANIFEST.in b/MANIFEST.in
index f45525bccf..735cc8d040 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,10 +1,4 @@
include requirements/*.txt
-include mmseg/VERSION
-include mmseg/model_zoo.yml
-include mmseg/configs/*/*.py
-include mmseg/configs/*/*.yml
-include mmseg/tools/*.py
-include mmseg/tools/*.sh
-include mmseg/tools/*/*.py
-include mmseg/demo/*/*
+recursive-include mmcls/configs *.py *.yml
+recursive-include mmcls/tools *.sh *.py
From ee12cf2329d4e1d9e667e4641d27535b6d44dc4b Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 15:19:08 +0800
Subject: [PATCH 09/29] modify manifest
---
MANIFEST.in | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/MANIFEST.in b/MANIFEST.in
index 735cc8d040..04c7fe77e1 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,4 @@
include requirements/*.txt
-recursive-include mmcls/configs *.py *.yml
-recursive-include mmcls/tools *.sh *.py
+recursive-include mmseg/configs *.py *.yml
+recursive-include mmseg/tools *.sh *.py
From 15cb1ba61989c16d9163f37b61c469266faf403e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E6=98=95=E8=BE=B0?= <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 15:41:56 +0800
Subject: [PATCH 10/29] Update MANIFEST.in
---
MANIFEST.in | 1 +
1 file changed, 1 insertion(+)
diff --git a/MANIFEST.in b/MANIFEST.in
index 04c7fe77e1..a1a7c9f8f5 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,5 @@
include requirements/*.txt
+include mmseg/model_zoo.yml
recursive-include mmseg/configs *.py *.yml
recursive-include mmseg/tools *.sh *.py
From e1a54407bf49dd1c20e46a9d03f2ce6e121bde23 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Wed, 12 May 2021 17:41:13 +0800
Subject: [PATCH 11/29] add metafile
---
configs/deeplabv3/metafile.yml | 422 +++++++++++++++++++++++++++
configs/fcn/metafile.yml | 503 ++++++++++++++++++++++++++++++++-
configs/pspnet/metafile.yml | 394 ++++++++++++++++++++++++++
3 files changed, 1317 insertions(+), 2 deletions(-)
create mode 100644 configs/deeplabv3/metafile.yml
create mode 100644 configs/pspnet/metafile.yml
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
new file mode 100644
index 0000000000..bb496064bf
--- /dev/null
+++ b/configs/deeplabv3/metafile.yml
@@ -0,0 +1,422 @@
+Collections:
+ - Name: DeepLabV3
+
+Modles:
+
+ - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 2.57
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.09
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 1.92
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.12
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: deeplabv3_r50-d8_769x769_40k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 1.11
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.58
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d8_769x769_40k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 0.83
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes
+
+
+
+ - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 13.78
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
+ Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.32
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.20
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r18-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 5.55
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.60
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
+ Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r50-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.89
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.67
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 6.96
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.71
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes
+
+
+
+ - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.36
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 13.93
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.26
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
+ Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 2.74
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.63
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
+ Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 1.81
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.01
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
+ Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 5.79
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.63
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
+ Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 1.16
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
+ Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 0.82
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
+ Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: deeplabv3_r50-d8_512x512_80k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 14.76
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.42
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k
+
+
+
+ - Name: deeplabv3_r101-d8_512x512_80k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 10.14
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 44.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k
+
+
+
+ - Name: deeplabv3_r50-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.66
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k
+
+
+
+ - Name: deeplabv3_r101-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.00
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k
+
+
+
+ - Name: deeplabv3_r50-d8_512x512_20k_voc12aug
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 13.88
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.17
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug
+
+
+
+ - Name: deeplabv3_r101-d8_512x512_20k_voc12aug
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 9.81
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug
+
+
+
+ - Name: deeplabv3_r50-d8_512x512_40k_voc12aug
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.68
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug
+
+
+
+ - Name: deeplabv3_r101-d8_512x512_40k_voc12aug
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.92
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug
+
+
+
+ - Name: deeplabv3_r101-d8_480x480_40k_pascal_context
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 7.09
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 46.55
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context
+
+
+
+ - Name: deeplabv3_r101-d8_480x480_80k_pascal_context
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 46.42
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context
+
+
+
+ - Name: deeplabv3_r101-d8_480x480_40k_pascal_context
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 52.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context
+
+
+
+ - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 52.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index 96ad802e9c..2bbcba6478 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -1,7 +1,506 @@
Collections:
- Name: FCN
-Models:
+
+Modles:
+
- Name: fcn_r50-d8_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
- Training Data: Cityscapes
+ inference time (fps): 4.17
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 72.25
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
+ Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: fcn_r101-d8_512x1024_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 2.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
+ Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: fcn_r50-d8_769x769_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 1.80
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 71.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
+ Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes
+
+
+
+ - Name: fcn_r101-d8_769x769_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 1.19
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 73.93
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
+ Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes
+
+
+
+ - Name: fcn_r18-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 14.65
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 71.11
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
+ Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r50-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 73.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
+ Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r101-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.13
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
+ Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r18-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 6.40
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 70.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
+ Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r50-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 72.64
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
+ Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r101-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.52
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
+ Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r18b-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 16.74
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 70.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
+ Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r50b-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 4.20
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.65
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
+ Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r101b-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 2.73
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.37
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
+ Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_r18b-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 6.70
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 69.66
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
+ Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r50b-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 1.82
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 73.83
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
+ Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r101b-d8_769x769_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 1.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
+ Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 10.22
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.06
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
+ Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes
+
+
+
+ - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 10.35
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
+ Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_d6_r50-d16_769x769_40k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 4.17
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.82
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
+ Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes
+
+
+
+ - Name: fcn_d6_r50-d16_769x769_80k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 4.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.04
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
+ Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 8.04
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.36
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
+ Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes
+
+
+
+ - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 8.26
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
+ Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes
+
+
+
+ - Name: fcn_d6_r101-d16_769x769_40k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 3.12
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.28
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
+ Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes
+
+
+
+ - Name: fcn_d6_r101-d16_769x769_80k_cityscapes
+ In Collection: FCN-D6
+ Metadata:
+ inference time (fps): 3.21
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.06
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
+ Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes
+
+
+
+ - Name: fcn_r50-d8_512x512_80k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 23.49
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 35.94
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
+ Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k
+
+
+
+ - Name: fcn_r101-d8_512x512_80k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 14.78
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 39.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
+ Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k
+
+
+
+ - Name: fcn_r50-d8_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 36.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
+ Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k
+
+
+
+ - Name: fcn_r101-d8_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 39.91
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
+ Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k
+
+
+
+ - Name: fcn_r50-d8_512x512_20k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 23.28
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 67.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
+ Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug
+
+
+
+ - Name: fcn_r101-d8_512x512_20k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 14.81
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 71.16
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
+ Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug
+
+
+
+ - Name: fcn_r50-d8_512x512_40k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 66.97
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
+ Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug
+
+
+
+ - Name: fcn_r101-d8_512x512_40k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 69.91
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
+ Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug
+
+
+
+ - Name: fcn_r101-d8_480x480_40k_pascal_context
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 9.93
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 44.43
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
+ Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context
+
+
+
+ - Name: fcn_r101-d8_480x480_80k_pascal_context
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 44.13
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
+ Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context
+
+
+
+ - Name: fcn_r101-d8_480x480_40k_pascal_context_59
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 48.42
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
+ Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59
+
+
+
+ - Name: fcn_r101-d8_480x480_80k_pascal_context_59
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 49.35
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
+ Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
new file mode 100644
index 0000000000..b112bcac41
--- /dev/null
+++ b/configs/pspnet/metafile.yml
@@ -0,0 +1,394 @@
+Collections:
+ - Name: PSPNet
+
+Modles:
+
+ - Name: pspnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 4.07
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.85
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: pspnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 2.68
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.34
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes
+
+
+
+ - Name: pspnet_r50-d8_769x769_40k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 1.76
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.26
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
+ Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes
+
+
+
+ - Name: pspnet_r101-d8_769x769_40k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 1.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
+ Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes
+
+
+
+ - Name: pspnet_r18-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 15.71
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
+ Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.55
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.76
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r18-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 6.20
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.90
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
+ Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r50-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.59
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
+ Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r101-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.77
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
+ Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r18b-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 16.28
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.23
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
+ Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r50b-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 4.30
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.22
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
+ Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r101b-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 2.76
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.69
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
+ Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes
+
+
+
+ - Name: pspnet_r18b-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 6.41
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.92
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
+ Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r50b-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 1.88
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.50
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
+ Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r101b-d8_769x769_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 1.17
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
+ Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes
+
+
+
+ - Name: pspnet_r50-d8_512x512_80k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 23.53
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.13
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k
+
+
+
+ - Name: pspnet_r101-d8_512x512_80k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 15.30
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.57
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k
+
+
+
+ - Name: pspnet_r50-d8_512x512_160k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.48
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k
+
+
+
+ - Name: pspnet_r101-d8_512x512_160k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 44.39
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k
+
+
+
+ - Name: pspnet_r50-d8_512x512_20k_voc12aug
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 23.59
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.78
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug
+
+
+
+ - Name: pspnet_r101-d8_512x512_20k_voc12aug
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 15.02
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug
+
+
+
+ - Name: pspnet_r50-d8_512x512_40k_voc12aug
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.29
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
+ Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug
+
+
+
+ - Name: pspnet_r101-d8_512x512_40k_voc12aug
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.52
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug
+
+
+
+ - Name: pspnet_r101-d8_480x480_40k_pascal_context
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 9.68
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 46.60
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
+ Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context
+
+
+
+ - Name: pspnet_r101-d8_480x480_80k_pascal_context
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 46.03
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
+ Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context
+
+
+
+ - Name: pspnet_r101-d8_480x480_40k_pascal_context
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 52.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
+ Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context
+
+
+
+ - Name: pspnet_r101-d8_480x480_80k_pascal_context_59
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 52.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
+ Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59
From c419a241528769d4f14028cbadcc2902d2808690 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 10:21:07 +0800
Subject: [PATCH 12/29] add metadata
---
configs/deeplabv3/metafile.yml | 2 ++
configs/fcn/metafile.yml | 2 ++
configs/pspnet/metafile.yml | 2 ++
3 files changed, 6 insertions(+)
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index bb496064bf..0c88ceb9df 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -1,5 +1,7 @@
Collections:
- Name: DeepLabV3
+ Metadata:
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Modles:
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index 2bbcba6478..b78b407fe7 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -1,5 +1,7 @@
Collections:
- Name: FCN
+ Metadata:
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Modles:
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
index b112bcac41..f42cf99d63 100644
--- a/configs/pspnet/metafile.yml
+++ b/configs/pspnet/metafile.yml
@@ -1,5 +1,7 @@
Collections:
- Name: PSPNet
+ Metadata:
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Modles:
From 249005e791b66354ceef8a6590d57a4bad7bd8d8 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 19:20:27 +0800
Subject: [PATCH 13/29] fix typo
---
configs/deeplabv3/metafile.yml | 2 +-
configs/fcn/metafile.yml | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index 0c88ceb9df..2530acadf6 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -3,7 +3,7 @@ Collections:
Metadata:
Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
-Modles:
+Models:
- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
In Collection: DeepLabV3
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index b78b407fe7..d53f802a3a 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -3,7 +3,7 @@ Collections:
Metadata:
Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
-Modles:
+Models:
- Name: fcn_r50-d8_512x1024_40k_cityscapes
In Collection: FCN
From 23e7424ecc47e0423b3c9a1aa35fc0ead01b58b8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E6=98=95=E8=BE=B0?= <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 19:36:44 +0800
Subject: [PATCH 14/29] Update metafile.yml
---
configs/fcn/metafile.yml | 3 +++
1 file changed, 3 insertions(+)
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index d53f802a3a..a576a6731b 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -2,6 +2,9 @@ Collections:
- Name: FCN
Metadata:
Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ - Name: FCN-D6
+ Metadata:
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Models:
From 146d72a03f7cbd61f8af122d51729ae4d5278b0e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E6=98=95=E8=BE=B0?= <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 19:40:10 +0800
Subject: [PATCH 15/29] Update metafile.yml
---
configs/fcn/metafile.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index a576a6731b..c26d785d8c 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -3,7 +3,7 @@ Collections:
Metadata:
Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
- Name: FCN-D6
- Metadata:
+ Metadata:
Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Models:
From 193391a21a9249385acb1739f217435f2e6cb7c2 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 20:01:48 +0800
Subject: [PATCH 16/29] minor change
---
configs/deeplabv3/metafile.yml | 6 +++++-
configs/fcn/metafile.yml | 6 +++++-
configs/pspnet/metafile.yml | 6 +++++-
3 files changed, 15 insertions(+), 3 deletions(-)
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index 2530acadf6..c304c3cec0 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -1,7 +1,11 @@
Collections:
- Name: DeepLabV3
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index d53f802a3a..9bbc1cb131 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -1,7 +1,11 @@
Collections:
- Name: FCN
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
index f42cf99d63..c862825290 100644
--- a/configs/pspnet/metafile.yml
+++ b/configs/pspnet/metafile.yml
@@ -1,7 +1,11 @@
Collections:
- Name: PSPNet
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Modles:
From 2c6b5c5e63bbbab0c83497d4724d6e046eaa9ac1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E6=98=95=E8=BE=B0?= <xinchen.xie@qq.com>
Date: Thu, 13 May 2021 20:05:39 +0800
Subject: [PATCH 17/29] Update metafile.yml
---
configs/fcn/metafile.yml | 7 +++++++
1 file changed, 7 insertions(+)
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index 9bbc1cb131..424e56d59d 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -6,6 +6,13 @@ Collections:
- Pascal Context
- Pascal VOC 2012 + Aug
- ADE20K
+ - Name: FCN-D6
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
From c5c0ef5a5a025d611afe0337acec85a56e0490ab Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 13:10:02 +0800
Subject: [PATCH 18/29] add subfix
---
configs/deeplabv3/metafile.yml | 66 +++++++++++++-------------
configs/fcn/metafile.yml | 85 +++++++++++++++-------------------
configs/pspnet/metafile.yml | 64 ++++++++++++-------------
3 files changed, 98 insertions(+), 117 deletions(-)
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index c304c3cec0..27d9b8cc61 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -1,11 +1,7 @@
Collections:
- Name: DeepLabV3
Metadata:
- Training Data:
- - Cityscapes
- - Pascal Context
- - Pascal VOC 2012 + Aug
- - ADE20K
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Models:
@@ -19,7 +15,7 @@ Models:
Metrics:
mIoU: 79.09
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
@@ -33,7 +29,7 @@ Models:
Metrics:
mIoU: 77.12
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
@@ -47,7 +43,7 @@ Models:
Metrics:
mIoU: 78.58
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
@@ -61,7 +57,7 @@ Models:
Metrics:
mIoU: 79.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
@@ -75,7 +71,7 @@ Models:
Metrics:
mIoU: 76.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
- Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
@@ -89,7 +85,7 @@ Models:
Metrics:
mIoU: 79.32
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
@@ -103,7 +99,7 @@ Models:
Metrics:
mIoU: 80.20
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
@@ -117,7 +113,7 @@ Models:
Metrics:
mIoU: 76.60
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
- Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
@@ -131,7 +127,7 @@ Models:
Metrics:
mIoU: 79.89
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
@@ -145,7 +141,7 @@ Models:
Metrics:
mIoU: 79.67
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
@@ -159,7 +155,7 @@ Models:
Metrics:
mIoU: 76.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth
- Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py
@@ -173,7 +169,7 @@ Models:
Metrics:
mIoU: 78.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
- Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
@@ -187,7 +183,7 @@ Models:
Metrics:
mIoU: 76.26
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
- Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
@@ -201,7 +197,7 @@ Models:
Metrics:
mIoU: 79.63
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
- Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
@@ -215,7 +211,7 @@ Models:
Metrics:
mIoU: 80.01
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
- Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
@@ -229,7 +225,7 @@ Models:
Metrics:
mIoU: 76.63
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
- Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
@@ -243,7 +239,7 @@ Models:
Metrics:
mIoU: 78.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
- Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
@@ -257,7 +253,7 @@ Models:
Metrics:
mIoU: 79.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
- Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes
+ Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
@@ -271,7 +267,7 @@ Models:
Metrics:
mIoU: 42.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
@@ -285,7 +281,7 @@ Models:
Metrics:
mIoU: 44.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
@@ -299,7 +295,7 @@ Models:
Metrics:
mIoU: 42.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
@@ -313,7 +309,7 @@ Models:
Metrics:
mIoU: 45.00
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
@@ -327,7 +323,7 @@ Models:
Metrics:
mIoU: 76.17
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
@@ -341,7 +337,7 @@ Models:
Metrics:
mIoU: 78.70
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
@@ -355,7 +351,7 @@ Models:
Metrics:
mIoU: 77.68
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug
+ Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
@@ -369,7 +365,7 @@ Models:
Metrics:
mIoU: 77.92
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
@@ -383,7 +379,7 @@ Models:
Metrics:
mIoU: 46.55
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
@@ -397,7 +393,7 @@ Models:
Metrics:
mIoU: 46.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
@@ -411,7 +407,7 @@ Models:
Metrics:
mIoU: 52.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
@@ -425,4 +421,4 @@ Models:
Metrics:
mIoU: 52.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59
+ Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index 424e56d59d..c60b169c93 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -1,18 +1,7 @@
Collections:
- Name: FCN
Metadata:
- Training Data:
- - Cityscapes
- - Pascal Context
- - Pascal VOC 2012 + Aug
- - ADE20K
- - Name: FCN-D6
- Metadata:
- Training Data:
- - Cityscapes
- - Pascal Context
- - Pascal VOC 2012 + Aug
- - ADE20K
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
Models:
@@ -26,7 +15,7 @@ Models:
Metrics:
mIoU: 72.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
- Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes
+ Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
@@ -40,7 +29,7 @@ Models:
Metrics:
mIoU: 75.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
- Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes
+ Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
@@ -54,7 +43,7 @@ Models:
Metrics:
mIoU: 71.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
- Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes
+ Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
@@ -68,7 +57,7 @@ Models:
Metrics:
mIoU: 73.93
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
- Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes
+ Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
@@ -82,7 +71,7 @@ Models:
Metrics:
mIoU: 71.11
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
- Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
@@ -96,7 +85,7 @@ Models:
Metrics:
mIoU: 73.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
- Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
@@ -110,7 +99,7 @@ Models:
Metrics:
mIoU: 75.13
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
- Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
@@ -124,7 +113,7 @@ Models:
Metrics:
mIoU: 70.80
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
- Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
@@ -138,7 +127,7 @@ Models:
Metrics:
mIoU: 72.64
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
- Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
@@ -152,7 +141,7 @@ Models:
Metrics:
mIoU: 75.52
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
- Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
@@ -166,7 +155,7 @@ Models:
Metrics:
mIoU: 70.24
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
- Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
@@ -180,7 +169,7 @@ Models:
Metrics:
mIoU: 75.65
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
- Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
@@ -194,7 +183,7 @@ Models:
Metrics:
mIoU: 77.37
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
- Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes
+ Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
@@ -208,7 +197,7 @@ Models:
Metrics:
mIoU: 69.66
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
- Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
@@ -222,7 +211,7 @@ Models:
Metrics:
mIoU: 73.83
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
- Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
@@ -236,7 +225,7 @@ Models:
Metrics:
mIoU: 77.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
- Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes
+ Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
@@ -250,7 +239,7 @@ Models:
Metrics:
mIoU: 77.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
- Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
@@ -264,7 +253,7 @@ Models:
Metrics:
mIoU: 77.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
- Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
@@ -278,7 +267,7 @@ Models:
Metrics:
mIoU: 76.82
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
- Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py
@@ -292,7 +281,7 @@ Models:
Metrics:
mIoU: 77.04
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
- Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py
@@ -306,7 +295,7 @@ Models:
Metrics:
mIoU: 77.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
- Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
@@ -320,7 +309,7 @@ Models:
Metrics:
mIoU: 78.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
- Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
@@ -334,7 +323,7 @@ Models:
Metrics:
mIoU: 77.28
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
- Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py
@@ -348,7 +337,7 @@ Models:
Metrics:
mIoU: 78.06
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
- Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes
+ Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py
@@ -362,7 +351,7 @@ Models:
Metrics:
mIoU: 35.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
- Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k
+ Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
@@ -376,7 +365,7 @@ Models:
Metrics:
mIoU: 39.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
- Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k
+ Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
@@ -390,7 +379,7 @@ Models:
Metrics:
mIoU: 36.10
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
- Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k
+ Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
@@ -404,7 +393,7 @@ Models:
Metrics:
mIoU: 39.91
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
- Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k
+ Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
@@ -418,7 +407,7 @@ Models:
Metrics:
mIoU: 67.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
- Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug
+ Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
@@ -432,7 +421,7 @@ Models:
Metrics:
mIoU: 71.16
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
- Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug
+ Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
@@ -446,7 +435,7 @@ Models:
Metrics:
mIoU: 66.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
- Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug
+ Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
@@ -460,7 +449,7 @@ Models:
Metrics:
mIoU: 69.91
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
- Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug
+ Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
@@ -474,7 +463,7 @@ Models:
Metrics:
mIoU: 44.43
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
- Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context
+ Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
@@ -488,7 +477,7 @@ Models:
Metrics:
mIoU: 44.13
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
- Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context
+ Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
@@ -502,7 +491,7 @@ Models:
Metrics:
mIoU: 48.42
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
- Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59
+ Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
@@ -516,4 +505,4 @@ Models:
Metrics:
mIoU: 49.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
- Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59
+ Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
index c862825290..7d9f53cb7a 100644
--- a/configs/pspnet/metafile.yml
+++ b/configs/pspnet/metafile.yml
@@ -1,13 +1,9 @@
Collections:
- Name: PSPNet
Metadata:
- Training Data:
- - Cityscapes
- - Pascal Context
- - Pascal VOC 2012 + Aug
- - ADE20K
+ Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
-Modles:
+Models:
- Name: pspnet_r50-d8_512x1024_40k_cityscapes
In Collection: PSPNet
@@ -19,7 +15,7 @@ Modles:
Metrics:
mIoU: 77.85
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
- Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes
+ Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
@@ -33,7 +29,7 @@ Modles:
Metrics:
mIoU: 78.34
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
- Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes
+ Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
@@ -47,7 +43,7 @@ Modles:
Metrics:
mIoU: 78.26
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
- Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes
+ Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
@@ -61,7 +57,7 @@ Modles:
Metrics:
mIoU: 79.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
- Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes
+ Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
@@ -75,7 +71,7 @@ Modles:
Metrics:
mIoU: 74.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
- Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
@@ -89,7 +85,7 @@ Modles:
Metrics:
mIoU: 78.55
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
- Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
@@ -103,7 +99,7 @@ Modles:
Metrics:
mIoU: 79.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
- Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
@@ -117,7 +113,7 @@ Modles:
Metrics:
mIoU: 75.90
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
- Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
@@ -131,7 +127,7 @@ Modles:
Metrics:
mIoU: 79.59
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
- Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
@@ -145,7 +141,7 @@ Modles:
Metrics:
mIoU: 79.77
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
- Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
@@ -159,7 +155,7 @@ Modles:
Metrics:
mIoU: 74.23
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
- Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
@@ -173,7 +169,7 @@ Modles:
Metrics:
mIoU: 78.22
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
- Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
@@ -187,7 +183,7 @@ Modles:
Metrics:
mIoU: 79.69
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
- Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes
+ Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
@@ -201,7 +197,7 @@ Modles:
Metrics:
mIoU: 74.92
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
- Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
@@ -215,7 +211,7 @@ Modles:
Metrics:
mIoU: 78.50
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
- Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
@@ -229,7 +225,7 @@ Modles:
Metrics:
mIoU: 78.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
- Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes
+ Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
@@ -243,7 +239,7 @@ Modles:
Metrics:
mIoU: 41.13
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k
+ Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
@@ -257,7 +253,7 @@ Modles:
Metrics:
mIoU: 43.57
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k
+ Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
@@ -271,7 +267,7 @@ Modles:
Metrics:
mIoU: 42.48
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k
+ Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
@@ -285,7 +281,7 @@ Modles:
Metrics:
mIoU: 44.39
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k
+ Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
@@ -299,7 +295,7 @@ Modles:
Metrics:
mIoU: 76.78
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug
+ Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
@@ -313,7 +309,7 @@ Modles:
Metrics:
mIoU: 78.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug
+ Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
@@ -327,7 +323,7 @@ Modles:
Metrics:
mIoU: 77.29
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
- Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug
+ Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
@@ -341,7 +337,7 @@ Modles:
Metrics:
mIoU: 78.52
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
- Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug
+ Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
@@ -355,7 +351,7 @@ Modles:
Metrics:
mIoU: 46.60
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context
+ Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
@@ -369,7 +365,7 @@ Modles:
Metrics:
mIoU: 46.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context
+ Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
@@ -383,7 +379,7 @@ Modles:
Metrics:
mIoU: 52.02
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context
+ Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
@@ -397,4 +393,4 @@ Modles:
Metrics:
mIoU: 52.47
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59
+ Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
From c005345816485f9a0198caf3d2544e17c39c9935 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 13:22:23 +0800
Subject: [PATCH 19/29] fix mmshow
---
configs/deeplabv3/metafile.yml | 6 +++++-
configs/fcn/metafile.yml | 13 ++++++++++++-
configs/pspnet/metafile.yml | 6 +++++-
3 files changed, 22 insertions(+), 3 deletions(-)
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index 27d9b8cc61..9f4da8946f 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -1,7 +1,11 @@
Collections:
- Name: DeepLabV3
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index c60b169c93..46e69820a5 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -1,7 +1,18 @@
Collections:
- Name: FCN
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+ - Name: FCN-D6
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
index 7d9f53cb7a..3823d5918a 100644
--- a/configs/pspnet/metafile.yml
+++ b/configs/pspnet/metafile.yml
@@ -1,7 +1,11 @@
Collections:
- Name: PSPNet
Metadata:
- Training Data: Cityscapes/Pascal Context/Pascal VOC 2012 + Aug/ADE20K
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
Models:
From d9903c31ed9ca68f29a767cd2e90a6adb92b62ea Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 14:49:18 +0800
Subject: [PATCH 20/29] add more metafile
---
configs/ann/metafile.yml | 231 ++++++++++++++++
configs/apcnet/metafile.yml | 174 ++++++++++++
configs/ccnet/metafile.yml | 231 ++++++++++++++++
configs/cgnet/metafile.yml | 33 +++
configs/danet/metafile.yml | 231 ++++++++++++++++
configs/deeplabv3plus/metafile.yml | 428 +++++++++++++++++++++++++++++
configs/dmnet/metafile.yml | 174 ++++++++++++
configs/dnlnet/metafile.yml | 174 ++++++++++++
configs/emanet/metafile.yml | 61 ++++
configs/encnet/metafile.yml | 175 ++++++++++++
configs/fastscnn/metafile.yml | 19 ++
configs/fp16/metafile.yml | 56 ++++
configs/gcnet/metafile.yml | 231 ++++++++++++++++
configs/hrnet/metafile.yml | 348 +++++++++++++++++++++++
configs/mobilenet_v2/metafile.yml | 112 ++++++++
configs/mobilenet_v3/metafile.yml | 56 ++++
configs/nonlocal_net/metafile.yml | 231 ++++++++++++++++
configs/ocrnet/metafile.yml | 343 +++++++++++++++++++++++
configs/point_rend/metafile.yml | 62 +++++
configs/psanet/metafile.yml | 231 ++++++++++++++++
configs/resnest/metafile.yml | 118 ++++++++
configs/sem_fpn/metafile.yml | 63 +++++
configs/unet/metafile.yml | 175 ++++++++++++
configs/upernet/metafile.yml | 231 ++++++++++++++++
24 files changed, 4188 insertions(+)
create mode 100644 configs/ann/metafile.yml
create mode 100644 configs/apcnet/metafile.yml
create mode 100644 configs/ccnet/metafile.yml
create mode 100644 configs/cgnet/metafile.yml
create mode 100644 configs/danet/metafile.yml
create mode 100644 configs/deeplabv3plus/metafile.yml
create mode 100644 configs/dmnet/metafile.yml
create mode 100644 configs/dnlnet/metafile.yml
create mode 100644 configs/emanet/metafile.yml
create mode 100644 configs/encnet/metafile.yml
create mode 100644 configs/fastscnn/metafile.yml
create mode 100644 configs/fp16/metafile.yml
create mode 100644 configs/gcnet/metafile.yml
create mode 100644 configs/hrnet/metafile.yml
create mode 100644 configs/mobilenet_v2/metafile.yml
create mode 100644 configs/mobilenet_v3/metafile.yml
create mode 100644 configs/nonlocal_net/metafile.yml
create mode 100644 configs/ocrnet/metafile.yml
create mode 100644 configs/point_rend/metafile.yml
create mode 100644 configs/psanet/metafile.yml
create mode 100644 configs/resnest/metafile.yml
create mode 100644 configs/sem_fpn/metafile.yml
create mode 100644 configs/unet/metafile.yml
create mode 100644 configs/upernet/metafile.yml
diff --git a/configs/ann/metafile.yml b/configs/ann/metafile.yml
new file mode 100644
index 0000000000..8ece7ee76e
--- /dev/null
+++ b/configs/ann/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: ANN
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: ann_r50-d8_512x1024_40k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 3.71
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.40
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
+ Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ann_r101-d8_512x1024_40k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 2.55
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.55
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
+ Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ann_r50-d8_769x769_40k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 1.70
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.89
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
+ Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: ann_r101-d8_769x769_40k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 1.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.32
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
+ Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: ann_r50-d8_512x1024_80k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.34
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
+ Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ann_r101-d8_512x1024_80k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.14
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
+ Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ann_r50-d8_769x769_80k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.88
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
+ Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: ann_r101-d8_769x769_80k_cityscapes
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
+ Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: ann_r50-d8_512x512_80k_ade20k
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 21.01
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.01
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
+ Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: ann_r101-d8_512x512_80k_ade20k
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 14.12
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.94
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
+ Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: ann_r50-d8_512x512_160k_ade20k
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.74
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
+ Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: ann_r101-d8_512x512_160k_ade20k
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.94
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
+ Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: ann_r50-d8_512x512_20k_voc12aug
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 20.92
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 74.86
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
+ Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: ann_r101-d8_512x512_20k_voc12aug
+ In Collection: ANN
+ Metadata:
+ inference time (fps): 13.94
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
+ Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: ann_r50-d8_512x512_40k_voc12aug
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.56
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
+ Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: ann_r101-d8_512x512_40k_voc12aug
+ In Collection: ANN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
+ Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/apcnet/metafile.yml b/configs/apcnet/metafile.yml
new file mode 100644
index 0000000000..f91635be85
--- /dev/null
+++ b/configs/apcnet/metafile.yml
@@ -0,0 +1,174 @@
+Collections:
+ - Name: APCNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - ADE20K
+
+Models:
+
+ - Name: apcnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 3.57
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
+ Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: apcnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 2.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
+ Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: apcnet_r50-d8_769x769_40k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 1.52
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.89
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
+ Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: apcnet_r101-d8_769x769_40k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 1.03
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.96
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
+ Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: apcnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.96
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
+ Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: apcnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.64
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
+ Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: apcnet_r50-d8_769x769_80k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.79
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
+ Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: apcnet_r101-d8_769x769_80k_cityscapes
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
+ Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: apcnet_r50-d8_512x512_80k_ade20k
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 19.61
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.20
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
+ Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: apcnet_r101-d8_512x512_80k_ade20k
+ In Collection: APCNet
+ Metadata:
+ inference time (fps): 13.10
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.54
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
+ Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: apcnet_r50-d8_512x512_160k_ade20k
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.40
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
+ Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: apcnet_r101-d8_512x512_160k_ade20k
+ In Collection: APCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
+ Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py
diff --git a/configs/ccnet/metafile.yml b/configs/ccnet/metafile.yml
new file mode 100644
index 0000000000..0f28967ea8
--- /dev/null
+++ b/configs/ccnet/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: CCNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: ccnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 3.32
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.76
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ccnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 2.31
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.35
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ccnet_r50-d8_769x769_40k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 1.43
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
+ Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: ccnet_r101-d8_769x769_40k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 1.01
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.94
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
+ Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: ccnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.03
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ccnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ccnet_r50-d8_769x769_80k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.29
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
+ Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: ccnet_r101-d8_769x769_80k_cityscapes
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
+ Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: ccnet_r50-d8_512x512_80k_ade20k
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 20.89
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.78
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: ccnet_r101-d8_512x512_80k_ade20k
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 14.11
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.97
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: ccnet_r50-d8_512x512_160k_ade20k
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: ccnet_r101-d8_512x512_160k_ade20k
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.71
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: ccnet_r50-d8_512x512_20k_voc12aug
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 20.45
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.17
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: ccnet_r101-d8_512x512_20k_voc12aug
+ In Collection: CCNet
+ Metadata:
+ inference time (fps): 13.64
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: ccnet_r50-d8_512x512_40k_voc12aug
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 75.96
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
+ Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: ccnet_r101-d8_512x512_40k_voc12aug
+ In Collection: CCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
+ Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/cgnet/metafile.yml b/configs/cgnet/metafile.yml
new file mode 100644
index 0000000000..29f1fbb416
--- /dev/null
+++ b/configs/cgnet/metafile.yml
@@ -0,0 +1,33 @@
+Collections:
+ - Name: CGNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+
+Models:
+
+ - Name: cgnet_680x680_60k_cityscapes
+ In Collection: CGNet
+ Metadata:
+ inference time (fps): 30.51
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 65.63
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
+ Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
+
+
+
+ - Name: cgnet_512x1024_60k_cityscapes
+ In Collection: CGNet
+ Metadata:
+ inference time (fps): 31.14
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 68.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
+ Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
diff --git a/configs/danet/metafile.yml b/configs/danet/metafile.yml
new file mode 100644
index 0000000000..a9e2b21139
--- /dev/null
+++ b/configs/danet/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: DANet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: danet_r50-d8_512x1024_40k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 2.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.74
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
+ Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: danet_r101-d8_512x1024_40k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 1.99
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.52
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
+ Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: danet_r50-d8_769x769_40k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 1.56
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.88
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
+ Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: danet_r101-d8_769x769_40k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 1.07
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.88
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
+ Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: danet_r50-d8_512x1024_80k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.34
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
+ Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: danet_r101-d8_512x1024_80k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
+ Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: danet_r50-d8_769x769_80k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
+ Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: danet_r101-d8_769x769_80k_cityscapes
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
+ Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: danet_r50-d8_512x512_80k_ade20k
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 21.20
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.66
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
+ Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: danet_r101-d8_512x512_80k_ade20k
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 14.18
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.64
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
+ Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: danet_r50-d8_512x512_160k_ade20k
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
+ Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: danet_r101-d8_512x512_160k_ade20k
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 44.17
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
+ Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: danet_r50-d8_512x512_20k_voc12aug
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 20.94
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 74.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
+ Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: danet_r101-d8_512x512_20k_voc12aug
+ In Collection: DANet
+ Metadata:
+ inference time (fps): 13.76
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
+ Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: danet_r50-d8_512x512_40k_voc12aug
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.37
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
+ Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: danet_r101-d8_512x512_40k_voc12aug
+ In Collection: DANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.51
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
+ Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/deeplabv3plus/metafile.yml b/configs/deeplabv3plus/metafile.yml
new file mode 100644
index 0000000000..4d3a72af30
--- /dev/null
+++ b/configs/deeplabv3plus/metafile.yml
@@ -0,0 +1,428 @@
+Collections:
+ - Name: DeepLabV3+
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal Context
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 3.94
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 2.60
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.21
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 1.72
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.97
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 1.15
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 14.27
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.89
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.09
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.97
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 5.74
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.26
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.83
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.98
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 7.48
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.09
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.90
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 14.95
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 3.94
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.28
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 2.60
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.16
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 5.96
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.36
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 1.72
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 1.10
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.88
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 21.01
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.72
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 14.16
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 44.60
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.95
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 21
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 75.93
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 13.88
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.22
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.81
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.62
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 9.09
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 47.30
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 47.23
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 52.86
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 53.2
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
diff --git a/configs/dmnet/metafile.yml b/configs/dmnet/metafile.yml
new file mode 100644
index 0000000000..ea7b7d070d
--- /dev/null
+++ b/configs/dmnet/metafile.yml
@@ -0,0 +1,174 @@
+Collections:
+ - Name: DMNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - ADE20K
+
+Models:
+
+ - Name: dmnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 3.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.78
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
+ Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: dmnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 2.54
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.37
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
+ Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: dmnet_r50-d8_769x769_40k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 1.57
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.49
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
+ Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: dmnet_r101-d8_769x769_40k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 1.01
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.62
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
+ Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: dmnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.07
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
+ Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: dmnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.64
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
+ Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: dmnet_r50-d8_769x769_80k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.22
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
+ Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: dmnet_r101-d8_769x769_80k_cityscapes
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.19
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
+ Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: dmnet_r50-d8_512x512_80k_ade20k
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 20.95
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.37
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
+ Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: dmnet_r101-d8_512x512_80k_ade20k
+ In Collection: DMNet
+ Metadata:
+ inference time (fps): 13.88
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.34
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
+ Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: dmnet_r50-d8_512x512_160k_ade20k
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.15
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
+ Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: dmnet_r101-d8_512x512_160k_ade20k
+ In Collection: DMNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.42
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
+ Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
diff --git a/configs/dnlnet/metafile.yml b/configs/dnlnet/metafile.yml
new file mode 100644
index 0000000000..bbb010d674
--- /dev/null
+++ b/configs/dnlnet/metafile.yml
@@ -0,0 +1,174 @@
+Collections:
+ - Name: dnl
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - ADE20K
+
+Models:
+
+ - Name: dnl_r50-d8_512x1024_40k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps): 2.56
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
+ Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: dnl_r101-d8_512x1024_40k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps): 1.96
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.31
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
+ Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: dnl_r50-d8_769x769_40k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps): 1.50
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.44
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
+ Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: dnl_r101-d8_769x769_40k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps): 1.02
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.39
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
+ Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: dnl_r50-d8_512x1024_80k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.33
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
+ Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: dnl_r101-d8_512x1024_80k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
+ Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: dnl_r50-d8_769x769_80k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.36
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
+ Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: dnl_r101-d8_769x769_80k_cityscapes
+ In Collection: dnl
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
+ Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: dnl_r50-d8_512x512_80k_ade20k
+ In Collection: DNL
+ Metadata:
+ inference time (fps): 20.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.76
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
+ Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: dnl_r101-d8_512x512_80k_ade20k
+ In Collection: DNL
+ Metadata:
+ inference time (fps): 12.54
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.76
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
+ Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: dnl_r50-d8_512x512_160k_ade20k
+ In Collection: DNL
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
+ Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: dnl_r101-d8_512x512_160k_ade20k
+ In Collection: DNL
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 44.25
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
+ Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py
diff --git a/configs/emanet/metafile.yml b/configs/emanet/metafile.yml
new file mode 100644
index 0000000000..f37dcec6d6
--- /dev/null
+++ b/configs/emanet/metafile.yml
@@ -0,0 +1,61 @@
+Collections:
+ - Name: EMANet
+ Metadata:
+ Training Data:
+ - Cityscapes
+
+Models:
+
+ - Name: emanet_r50-d8_512x1024_80k_cityscapes
+ In Collection: EMANet
+ Metadata:
+ inference time (fps): 4.58
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.59
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
+ Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: emanet_r101-d8_512x1024_80k_cityscapes
+ In Collection: EMANet
+ Metadata:
+ inference time (fps): 2.87
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
+ Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: emanet_r50-d8_769x769_80k_cityscapes
+ In Collection: EMANet
+ Metadata:
+ inference time (fps): 1.97
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.33
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
+ Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: emanet_r101-d8_769x769_80k_cityscapes
+ In Collection: EMANet
+ Metadata:
+ inference time (fps): 1.22
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.62
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
+ Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
diff --git a/configs/encnet/metafile.yml b/configs/encnet/metafile.yml
new file mode 100644
index 0000000000..dbb8a542d8
--- /dev/null
+++ b/configs/encnet/metafile.yml
@@ -0,0 +1,175 @@
+Collections:
+ - Name: encnet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: encnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 4.58
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.67
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
+ Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: encnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 2.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.81
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
+ Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: encnet_r50-d8_769x769_40k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 1.82
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
+ Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: encnet_r101-d8_769x769_40k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 1.26
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.25
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
+ Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: encnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.94
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
+ Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: encnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.55
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
+ Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: encnet_r50-d8_769x769_80k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.44
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
+ Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: encnet_r101-d8_769x769_80k_cityscapes
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
+ Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: encnet_r50-d8_512x512_80k_ade20k
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 22.81
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 39.53
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
+ Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: encnet_r101-d8_512x512_80k_ade20k
+ In Collection: encnet
+ Metadata:
+ inference time (fps): 14.87
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.11
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
+ Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: encnet_r50-d8_512x512_160k_ade20k
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 40.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
+ Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: encnet_r101-d8_512x512_160k_ade20k
+ In Collection: encnet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
+ Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
diff --git a/configs/fastscnn/metafile.yml b/configs/fastscnn/metafile.yml
new file mode 100644
index 0000000000..edae6f6aa3
--- /dev/null
+++ b/configs/fastscnn/metafile.yml
@@ -0,0 +1,19 @@
+Collections:
+ - Name: Fast-SCNN
+ Metadata:
+ Training Data:
+ - Cityscapes
+
+Models:
+
+ - Name: fast_scnn_4x8_80k_lr0.12_cityscapes
+ In Collection: Fast-SCNN
+ Metadata:
+ inference time (fps): 63.61
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 69.06
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth
+ Config: configs/fast-scnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py
diff --git a/configs/fp16/metafile.yml b/configs/fp16/metafile.yml
new file mode 100644
index 0000000000..e4187bdad2
--- /dev/null
+++ b/configs/fp16/metafile.yml
@@ -0,0 +1,56 @@
+
+Models:
+
+ - Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 8.64
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth
+ Config: configs/fcn/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
+
+
+
+ - Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 8.77
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth
+ Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
+
+
+
+ - Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 3.86
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.48
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth
+ Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 7.87
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.46
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth
+ Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml
new file mode 100644
index 0000000000..03d78931a7
--- /dev/null
+++ b/configs/gcnet/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: GCNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: gcnet_r50-d8_512x1024_40k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 3.93
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.69
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: gcnet_r101-d8_512x1024_40k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 2.61
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.28
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: gcnet_r50-d8_769x769_40k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 1.67
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.12
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
+ Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: gcnet_r101-d8_769x769_40k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 1.13
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.95
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
+ Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: gcnet_r50-d8_512x1024_80k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.48
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: gcnet_r101-d8_512x1024_80k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.03
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: gcnet_r50-d8_769x769_80k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.68
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
+ Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: gcnet_r101-d8_769x769_80k_cityscapes
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.18
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
+ Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: gcnet_r50-d8_512x512_80k_ade20k
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 23.38
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: gcnet_r101-d8_512x512_80k_ade20k
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 15.20
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.82
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: gcnet_r50-d8_512x512_160k_ade20k
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.37
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: gcnet_r101-d8_512x512_160k_ade20k
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.69
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: gcnet_r50-d8_512x512_20k_voc12aug
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 23.35
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.42
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: gcnet_r101-d8_512x512_20k_voc12aug
+ In Collection: GCNet
+ Metadata:
+ inference time (fps): 14.80
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.41
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: gcnet_r50-d8_512x512_40k_voc12aug
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
+ Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: gcnet_r101-d8_512x512_40k_voc12aug
+ In Collection: GCNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.84
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
+ Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml
new file mode 100644
index 0000000000..b2145845ca
--- /dev/null
+++ b/configs/hrnet/metafile.yml
@@ -0,0 +1,348 @@
+
+ - Name: fcn_hr18s_512x1024_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 23.74
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 73.86
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
+ Config: configs/fcn/fcn_hr18s_512x1024_40k_cityscapes.py
+
+
+
+ - Name: fcn_hr18_512x1024_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 12.97
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.19
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
+ Config: configs/fcn/fcn_hr18_512x1024_40k_cityscapes.py
+
+
+
+ - Name: fcn_hr48_512x1024_40k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 6.42
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.48
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
+ Config: configs/fcn/fcn_hr48_512x1024_40k_cityscapes.py
+
+
+
+ - Name: fcn_hr18s_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.31
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
+ Config: configs/fcn/fcn_hr18s_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fcn_hr18_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.65
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
+ Config: configs/fcn/fcn_hr18_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fcn_hr48_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.93
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
+ Config: configs/fcn/fcn_hr48_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fcn_hr18s_512x1024_160k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.31
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
+ Config: configs/fcn/fcn_hr18s_512x1024_160k_cityscapes.py
+
+
+
+ - Name: fcn_hr18_512x1024_160k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
+ Config: configs/fcn/fcn_hr18_512x1024_160k_cityscapes.py
+
+
+
+ - Name: fcn_hr48_512x1024_160k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.65
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
+ Config: configs/fcn/fcn_hr48_512x1024_160k_cityscapes.py
+
+
+
+ - Name: fcn_hr18s_512x512_80k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 38.66
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 31.38
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
+ Config: configs/fcn/fcn_hr18s_512x512_80k_ade20k.py
+
+
+
+ - Name: fcn_hr18_512x512_80k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 22.57
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 35.51
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth
+ Config: configs/fcn/fcn_hr18_512x512_80k_ade20k.py
+
+
+
+ - Name: fcn_hr48_512x512_80k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 21.23
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.90
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
+ Config: configs/fcn/fcn_hr48_512x512_80k_ade20k.py
+
+
+
+ - Name: fcn_hr18s_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 33.00
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth
+ Config: configs/fcn/fcn_hr18s_512x512_160k_ade20k.py
+
+
+
+ - Name: fcn_hr18_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 36.79
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
+ Config: configs/fcn/fcn_hr18_512x512_160k_ade20k.py
+
+
+
+ - Name: fcn_hr48_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
+ Config: configs/fcn/fcn_hr48_512x512_160k_ade20k.py
+
+
+
+ - Name: fcn_hr18s_512x512_20k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 43.36
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 65.20
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth
+ Config: configs/fcn/fcn_hr18s_512x512_20k_voc12aug.py
+
+
+
+ - Name: fcn_hr18_512x512_20k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 23.48
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 72.30
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
+ Config: configs/fcn/fcn_hr18_512x512_20k_voc12aug.py
+
+
+
+ - Name: fcn_hr48_512x512_20k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 22.05
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 75.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
+ Config: configs/fcn/fcn_hr48_512x512_20k_voc12aug.py
+
+
+
+ - Name: fcn_hr18s_512x512_40k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 66.61
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
+ Config: configs/fcn/fcn_hr18s_512x512_40k_voc12aug.py
+
+
+
+ - Name: fcn_hr18_512x512_40k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 72.90
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
+ Config: configs/fcn/fcn_hr18_512x512_40k_voc12aug.py
+
+
+
+ - Name: fcn_hr48_512x512_40k_voc12aug
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
+ Config: configs/fcn/fcn_hr48_512x512_40k_voc12aug.py
+
+
+
+ - Name: fcn_hr48_480x480_40k_pascal_context
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 8.86
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 45.14
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
+ Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context.py
+
+
+
+ - Name: fcn_hr48_480x480_80k_pascal_context
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 45.84
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
+ Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py
+
+
+
+ - Name: fcn_hr48_480x480_40k_pascal_context_59
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 50.33
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
+ Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context_59.py
+
+
+
+ - Name: fcn_hr48_480x480_80k_pascal_context
+ In Collection: FCN
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal Context
+ Metrics:
+ mIoU: 51.12
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
+ Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py
diff --git a/configs/mobilenet_v2/metafile.yml b/configs/mobilenet_v2/metafile.yml
new file mode 100644
index 0000000000..7146869385
--- /dev/null
+++ b/configs/mobilenet_v2/metafile.yml
@@ -0,0 +1,112 @@
+
+Models:
+
+ - Name: fcn_m-v2-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 14.2
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 61.54
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
+ Config: configs/fcn/fcn_m-v2-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 11.2
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 70.23
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
+ Config: configs/pspnet/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 8.4
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 73.84
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
+ Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 8.4
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.20
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
+ Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fcn_m-v2-d8_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 64.4
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 19.71
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
+ Config: configs/fcn/fcn_m-v2-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: pspnet_m-v2-d8_512x512_160k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 57.7
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 29.68
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
+ Config: configs/pspnet/pspnet_m-v2-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 39.9
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 34.08
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
+ Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 43.1
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 34.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
+ Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
diff --git a/configs/mobilenet_v3/metafile.yml b/configs/mobilenet_v3/metafile.yml
new file mode 100644
index 0000000000..6a9e92ea8c
--- /dev/null
+++ b/configs/mobilenet_v3/metafile.yml
@@ -0,0 +1,56 @@
+
+Models:
+
+ - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
+ In Collection: LRASPP
+ Metadata:
+ inference time (fps): 15.22
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 69.54
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
+ Config: configs/lraspp/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
+
+
+
+ - Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
+ In Collection: LRASPP
+ Metadata:
+ inference time (fps): 14.77
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 67.87
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
+ Config: configs/lraspp/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
+
+
+
+ - Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
+ In Collection: LRASPP
+ Metadata:
+ inference time (fps): 23.64
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 64.11
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
+ Config: configs/lraspp/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
+
+
+
+ - Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
+ In Collection: LRASPP
+ Metadata:
+ inference time (fps): 24.50
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 62.74
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
+ Config: configs/lraspp/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
diff --git a/configs/nonlocal_net/metafile.yml b/configs/nonlocal_net/metafile.yml
new file mode 100644
index 0000000000..4c545ebab0
--- /dev/null
+++ b/configs/nonlocal_net/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: NonLocal
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: nonlocal_r50-d8_512x1024_40k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 2.72
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: nonlocal_r101-d8_512x1024_40k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 1.95
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.66
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: nonlocal_r50-d8_769x769_40k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 1.52
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.33
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: nonlocal_r101-d8_769x769_40k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 1.05
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.57
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: nonlocal_r50-d8_512x1024_80k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.01
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: nonlocal_r101-d8_512x1024_80k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.93
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: nonlocal_r50-d8_769x769_80k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.05
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: nonlocal_r101-d8_769x769_80k_cityscapes
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.40
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: nonlocal_r50-d8_512x512_80k_ade20k
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 21.37
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 40.75
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: nonlocal_r101-d8_512x512_80k_ade20k
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 13.97
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.90
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: nonlocal_r50-d8_512x512_160k_ade20k
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.03
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: nonlocal_r101-d8_512x512_160k_ade20k
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.36
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: nonlocal_r50-d8_512x512_20k_voc12aug
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 21.21
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.20
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: nonlocal_r101-d8_512x512_20k_voc12aug
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps): 14.01
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.15
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: nonlocal_r50-d8_512x512_40k_voc12aug
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.65
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
+ Config: configs/nonlocal/nonlocal_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: nonlocal_r101-d8_512x512_40k_voc12aug
+ In Collection: NonLocal
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 78.27
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
+ Config: configs/nonlocal/nonlocal_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/ocrnet/metafile.yml b/configs/ocrnet/metafile.yml
new file mode 100644
index 0000000000..50b6d0a5ed
--- /dev/null
+++ b/configs/ocrnet/metafile.yml
@@ -0,0 +1,343 @@
+Collections:
+ - Name: OCRNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: ocrnet_hr18s_512x1024_40k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 10.45
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.30
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18_512x1024_40k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 7.50
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.72
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr48_512x1024_40k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 4.22
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.58
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18s_512x1024_80k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.16
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18_512x1024_80k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.57
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr48_512x1024_80k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18s_512x1024_160k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.45
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18_512x1024_160k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
+
+
+
+ - Name: ocrnet_hr48_512x1024_160k_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 81.35
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
+
+
+
+ - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU:
+ Weights: https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
+ Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
+
+
+
+ - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 8.8
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 3.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
+ Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
+
+
+
+ - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 8.8
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 3.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
+ Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
+
+
+
+ - Name: ocrnet_hr18s_512x512_80k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 28.98
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 35.06
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
+
+
+
+ - Name: ocrnet_hr18_512x512_80k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 18.93
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 37.79
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
+
+
+
+ - Name: ocrnet_hr48_512x512_80k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 16.99
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.00
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
+
+
+
+ - Name: ocrnet_hr18s_512x512_160k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 37.19
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
+
+
+
+ - Name: ocrnet_hr18_512x512_160k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 39.32
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+
+
+
+ - Name: ocrnet_hr48_512x512_160k_ade20k
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.25
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
+
+
+
+ - Name: ocrnet_hr18s_512x512_20k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 31.55
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 71.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
+
+
+
+ - Name: ocrnet_hr18_512x512_20k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 19.91
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 74.75
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
+
+
+
+ - Name: ocrnet_hr48_512x512_20k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps): 17.83
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.72
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
+
+
+
+ - Name: ocrnet_hr18s_512x512_40k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 72.76
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
+ Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
+
+
+
+ - Name: ocrnet_hr18_512x512_40k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 74.98
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
+ Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
+
+
+
+ - Name: ocrnet_hr48_512x512_40k_voc12aug
+ In Collection: OCRNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.14
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
+ Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml
new file mode 100644
index 0000000000..aba00e0931
--- /dev/null
+++ b/configs/point_rend/metafile.yml
@@ -0,0 +1,62 @@
+Collections:
+ - Name: PointRend
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - ADE20K
+
+Models:
+
+ - Name: pointrend_r50_512x1024_80k_cityscapes
+ In Collection: PointRend
+ Metadata:
+ inference time (fps): 8.48
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 76.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
+ Config: configs/pointrend/pointrend_r50_512x1024_80k_cityscapes.py
+
+
+
+ - Name: pointrend_r101_512x1024_80k_cityscapes
+ In Collection: PointRend
+ Metadata:
+ inference time (fps): 7.00
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.30
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
+ Config: configs/pointrend/pointrend_r101_512x1024_80k_cityscapes.py
+
+
+
+ - Name: pointrend_r50_512x512_160k_ade20k
+ In Collection: PointRend
+ Metadata:
+ inference time (fps): 17.31
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 37.64
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
+ Config: configs/pointrend/pointrend_r50_512x512_160k_ade20k.py
+
+
+
+ - Name: pointrend_r101_512x512_160k_ade20k
+ In Collection: PointRend
+ Metadata:
+ inference time (fps): 15.50
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 40.02
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
+ Config: configs/pointrend/pointrend_r101_512x512_160k_ade20k.py
diff --git a/configs/psanet/metafile.yml b/configs/psanet/metafile.yml
new file mode 100644
index 0000000000..1052ec1e19
--- /dev/null
+++ b/configs/psanet/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: PSANet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: psanet_r50-d8_512x1024_40k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 3.17
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.63
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
+ Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: psanet_r101-d8_512x1024_40k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 2.20
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.14
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
+ Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
+
+
+
+ - Name: psanet_r50-d8_769x769_40k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 1.40
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.99
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
+ Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: psanet_r101-d8_769x769_40k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 0.98
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.43
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
+ Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
+
+
+
+ - Name: psanet_r50-d8_512x1024_80k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.24
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
+ Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: psanet_r101-d8_512x1024_80k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.31
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
+ Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: psanet_r50-d8_769x769_80k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.31
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
+ Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: psanet_r101-d8_769x769_80k_cityscapes
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.69
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
+ Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
+
+
+
+ - Name: psanet_r50-d8_512x512_80k_ade20k
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 18.91
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.14
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
+ Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: psanet_r101-d8_512x512_80k_ade20k
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 13.13
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
+ Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
+
+
+
+ - Name: psanet_r50-d8_512x512_160k_ade20k
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 41.67
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
+ Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: psanet_r101-d8_512x512_160k_ade20k
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.74
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
+ Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: psanet_r50-d8_512x512_20k_voc12aug
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 18.24
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.39
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
+ Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: psanet_r101-d8_512x512_20k_voc12aug
+ In Collection: PSANet
+ Metadata:
+ inference time (fps): 12.63
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.91
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
+ Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
+
+
+
+ - Name: psanet_r50-d8_512x512_40k_voc12aug
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 76.30
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
+ Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
+
+
+
+ - Name: psanet_r101-d8_512x512_40k_voc12aug
+ In Collection: PSANet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.73
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
+ Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml
new file mode 100644
index 0000000000..d6775ac9d5
--- /dev/null
+++ b/configs/resnest/metafile.yml
@@ -0,0 +1,118 @@
+Collections:
+ - Name: ResNeSt
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - ADE20K
+
+Models:
+
+ - Name: fcn_s101-d8_512x1024_80k_cityscapes
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 2.39
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.56
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
+ Config: configs/fcn/fcn_s101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: pspnet_s101-d8_512x1024_80k_cityscapes
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 2.52
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.57
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
+ Config: configs/pspnet/pspnet_s101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 1.88
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.67
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
+ Config: configs/deeplabv3/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 2.36
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.62
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
+ Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fcn_s101-d8_512x512_160k_ade20k
+ In Collection: FCN
+ Metadata:
+ inference time (fps): 12.86
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.62
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
+ Config: configs/fcn/fcn_s101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: pspnet_s101-d8_512x512_160k_ade20k
+ In Collection: PSPNet
+ Metadata:
+ inference time (fps): 13.02
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.44
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
+ Config: configs/pspnet/pspnet_s101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3_s101-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3
+ Metadata:
+ inference time (fps): 9.28
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 45.71
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
+ Config: configs/deeplabv3/deeplabv3_s101-d8_512x512_160k_ade20k.py
+
+
+
+ - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
+ In Collection: DeepLabV3+
+ Metadata:
+ inference time (fps): 11.96
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 46.47
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
+ Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
diff --git a/configs/sem_fpn/metafile.yml b/configs/sem_fpn/metafile.yml
new file mode 100644
index 0000000000..9bbb04be19
--- /dev/null
+++ b/configs/sem_fpn/metafile.yml
@@ -0,0 +1,63 @@
+Collections:
+ - Name: SEM FPN
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: fpn_r50_512x1024_80k_cityscapes
+ In Collection: FPN
+ Metadata:
+ inference time (fps): 13.54
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 74.52
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
+ Config: configs/fpn/fpn_r50_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fpn_r101_512x1024_80k_cityscapes
+ In Collection: FPN
+ Metadata:
+ inference time (fps): 10.29
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 75.80
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
+ Config: configs/fpn/fpn_r101_512x1024_80k_cityscapes.py
+
+
+
+ - Name: fpn_r50_512x512_160k_ade20k
+ In Collection: FPN
+ Metadata:
+ inference time (fps): 55.77
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 37.49
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
+ Config: configs/fpn/fpn_r50_512x512_160k_ade20k.py
+
+
+
+ - Name: fpn_r101_512x512_160k_ade20k
+ In Collection: FPN
+ Metadata:
+ inference time (fps): 40.58
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 39.35
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
+ Config: configs/fpn/fpn_r101_512x512_160k_ade20k.py
diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml
new file mode 100644
index 0000000000..932ef287ce
--- /dev/null
+++ b/configs/unet/metafile.yml
@@ -0,0 +1,175 @@
+Collections:
+ - Name: UPerNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: fcn_unet_s5-d16_64x64_40k_drive
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.680
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth
+ Config: configs/unet-s5-d16/fcn_unet_s5-d16_64x64_40k_drive.py
+
+
+
+ - Name: pspnet_unet_s5-d16_64x64_40k_drive
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.599
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
+ Config: configs/unet-s5-d16/pspnet_unet_s5-d16_64x64_40k_drive.py
+
+
+
+ - Name: deeplabv3_unet_s5-d16_64x64_40k_drive
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.596
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
+ Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_64x64_40k_drive.py
+
+
+
+ - Name: fcn_unet_s5-d16_128x128_40k_stare
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.968
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth
+ Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_stare.py
+
+
+
+ - Name: pspnet_unet_s5-d16_128x128_40k_stare
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.982
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
+ Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_stare.py
+
+
+
+ - Name: deeplabv3_unet_s5-d16_128x128_40k_stare
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.999
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
+ Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_stare.py
+
+
+
+ - Name: fcn_unet_s5-d16_128x128_40k_chase_db1
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.968
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth
+ Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_chase_db1.py
+
+
+
+ - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.982
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
+ Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
+
+
+
+ - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 0.999
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
+ Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
+
+
+
+ - Name: fcn_unet_s5-d16_256x256_40k_hrf
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 2.525
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth
+ Config: configs/unet-s5-d16/fcn_unet_s5-d16_256x256_40k_hrf.py
+
+
+
+ - Name: pspnet_unet_s5-d16_256x256_40k_hrf
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 2.588
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
+ Config: configs/unet-s5-d16/pspnet_unet_s5-d16_256x256_40k_hrf.py
+
+
+
+ - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
+ In Collection: UNet-S5-D16
+ Metadata:
+ inference time (fps): 40000
+ Results:
+ - Task: Semantic Segmentation
+ Dataset:
+ Metrics:
+ mIoU: 2.604
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
+ Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
diff --git a/configs/upernet/metafile.yml b/configs/upernet/metafile.yml
new file mode 100644
index 0000000000..7c6773febc
--- /dev/null
+++ b/configs/upernet/metafile.yml
@@ -0,0 +1,231 @@
+Collections:
+ - Name: UPerNet
+ Metadata:
+ Training Data:
+ - Cityscapes
+ - Pascal VOC 2012 + Aug
+ - ADE20K
+
+Models:
+
+ - Name: upernet_r50_512x1024_40k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 4.25
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
+ Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
+
+
+
+ - Name: upernet_r101_512x1024_40k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 3.79
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.69
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
+ Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
+
+
+
+ - Name: upernet_r50_769x769_40k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 1.76
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 77.98
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
+ Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
+
+
+
+ - Name: upernet_r101_769x769_40k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 1.56
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.03
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
+ Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
+
+
+
+ - Name: upernet_r50_512x1024_80k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 78.19
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
+ Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
+
+
+
+ - Name: upernet_r101_512x1024_80k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.40
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
+ Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
+
+
+
+ - Name: upernet_r50_769x769_80k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 79.39
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
+ Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
+
+
+
+ - Name: upernet_r101_769x769_80k_cityscapes
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Cityscapes
+ Metrics:
+ mIoU: 80.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
+ Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
+
+
+
+ - Name: upernet_r50_512x512_80k_ade20k
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 23.40
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 40.70
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
+ Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
+
+
+
+ - Name: upernet_r101_512x512_80k_ade20k
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 20.34
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.91
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
+ Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
+
+
+
+ - Name: upernet_r50_512x512_160k_ade20k
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 42.05
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
+ Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
+
+
+
+ - Name: upernet_r101_512x512_160k_ade20k
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: ADE20K
+ Metrics:
+ mIoU: 43.82
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
+ Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
+
+
+
+ - Name: upernet_r50_512x512_20k_voc12aug
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 23.17
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 74.82
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
+ Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
+
+
+
+ - Name: upernet_r101_512x512_20k_voc12aug
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps): 19.98
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.10
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
+ Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
+
+
+
+ - Name: upernet_r50_512x512_40k_voc12aug
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 75.92
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
+ Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
+
+
+
+ - Name: upernet_r101_512x512_40k_voc12aug
+ In Collection: UPerNet
+ Metadata:
+ inference time (fps):
+ Results:
+ - Task: Semantic Segmentation
+ Dataset: Pascal VOC 2012 + Aug
+ Metrics:
+ mIoU: 77.43
+ Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
+ Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
From 06ea96b7fd56a5a5615e41308e7a72e6785f4b0a Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 15:06:41 +0800
Subject: [PATCH 21/29] add config to model_zoo
---
model_zoo.yml | 25 ++++++++++++++++++++++++-
1 file changed, 24 insertions(+), 1 deletion(-)
diff --git a/model_zoo.yml b/model_zoo.yml
index aae808abb7..6a95f49c32 100644
--- a/model_zoo.yml
+++ b/model_zoo.yml
@@ -1,4 +1,27 @@
Import:
+ - configs/ann/metafile.yml
+ - configs/apcnet/metafile.yml
+ - configs/ccnet/metafile.yml
+ - configs/cgnet/metafile.yml
+ - configs/danet/metafile.yml
+ - configs/deeplabv3/metafile.yml
+ - configs/deeplabv3plus/metafile.yml
+ - configs/dnlnet/metafile.yml
+ - configs/emanet/metafile.yml
+ - configs/encnet/metafile.yml
+ - configs/fastscnn/metafile.yml
- configs/fcn/metafile.yml
+ - configs/fp16/metafile.yml
+ - configs/gcnet/metafile.yml
+ - configs/hrnet/metafile.yml
+ - configs/mobilenet_v2/metafile.yml
+ - configs/mobilenet_v3/metafile.yml
+ - configs/nonlocal_net/metafile.yml
+ - configs/ocrnet/metafile.yml
+ - configs/point_rend/metafile.yml
+ - configs/psanet/metafile.yml
- configs/pspnet/metafile.yml
- - configs/deeplabv3/metafile.yml
+ - configs/resnest/metafile.yml
+ - configs/sem_fpn/metafile.yml
+ - configs/unet/metafile.yml
+ - configs/upernet/metafile.yml
From 708a6c212ce4f36f8d90c5d7d639b4701087223e Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 15:29:02 +0800
Subject: [PATCH 22/29] fix bug
---
configs/dnlnet/metafile.yml | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/configs/dnlnet/metafile.yml b/configs/dnlnet/metafile.yml
index bbb010d674..9a05dfbb2b 100644
--- a/configs/dnlnet/metafile.yml
+++ b/configs/dnlnet/metafile.yml
@@ -120,7 +120,7 @@ Models:
- Name: dnl_r50-d8_512x512_80k_ade20k
- In Collection: DNL
+ In Collection: dnl
Metadata:
inference time (fps): 20.66
Results:
@@ -134,7 +134,7 @@ Models:
- Name: dnl_r101-d8_512x512_80k_ade20k
- In Collection: DNL
+ In Collection: dnl
Metadata:
inference time (fps): 12.54
Results:
@@ -148,7 +148,7 @@ Models:
- Name: dnl_r50-d8_512x512_160k_ade20k
- In Collection: DNL
+ In Collection: dnl
Metadata:
inference time (fps):
Results:
@@ -162,7 +162,7 @@ Models:
- Name: dnl_r101-d8_512x512_160k_ade20k
- In Collection: DNL
+ In Collection: dnl
Metadata:
inference time (fps):
Results:
From d32a4a6d7b6c35e523ee57864063194530334fbe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=B0=A2=E6=98=95=E8=BE=B0?= <xinchen.xie@qq.com>
Date: Sat, 15 May 2021 15:51:38 +0800
Subject: [PATCH 23/29] Update mminstall.txt
---
requirements/mminstall.txt | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt
index d371e1cc8e..b1c42eb464 100644
--- a/requirements/mminstall.txt
+++ b/requirements/mminstall.txt
@@ -1 +1 @@
-mmcv-full>=1.3.0
+mmcv-full>=1.3.1,<=1.4.0
From 605d3e643d199492d735aa3992df1bd72d79cb2e Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 24 May 2021 14:23:04 +0800
Subject: [PATCH 24/29] [fix] Add models
---
configs/hrnet/metafile.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml
index b2145845ca..ed33e2d7d9 100644
--- a/configs/hrnet/metafile.yml
+++ b/configs/hrnet/metafile.yml
@@ -1,4 +1,4 @@
-
+Models:
- Name: fcn_hr18s_512x1024_40k_cityscapes
In Collection: FCN
Metadata:
From 063efd15ff6efb1148d563f84268b00a4726a75f Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 24 May 2021 14:32:03 +0800
Subject: [PATCH 25/29] [Fix] Add collections
---
configs/mobilenet_v3/metafile.yml | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/configs/mobilenet_v3/metafile.yml b/configs/mobilenet_v3/metafile.yml
index 6a9e92ea8c..efd700058e 100644
--- a/configs/mobilenet_v3/metafile.yml
+++ b/configs/mobilenet_v3/metafile.yml
@@ -1,3 +1,8 @@
+Collections:
+ - Name: LRASPP
+ Metadata:
+ Training Data:
+ - Cityscapes
Models:
From 478522498d3ea00dfced352a8bfcdcf630263ddb Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 24 May 2021 14:38:05 +0800
Subject: [PATCH 26/29] [fix] Modify collection name
---
configs/sem_fpn/metafile.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/configs/sem_fpn/metafile.yml b/configs/sem_fpn/metafile.yml
index 9bbb04be19..781589ac0b 100644
--- a/configs/sem_fpn/metafile.yml
+++ b/configs/sem_fpn/metafile.yml
@@ -1,5 +1,5 @@
Collections:
- - Name: SEM FPN
+ - Name: FPN
Metadata:
Training Data:
- Cityscapes
From 7473cbe47673f875026533ad9353c9db12127c64 Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 24 May 2021 14:47:41 +0800
Subject: [PATCH 27/29] [Fix] Set datasets to unet metafile
---
configs/unet/metafile.yml | 24 ++++++++++++------------
1 file changed, 12 insertions(+), 12 deletions(-)
diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml
index 932ef287ce..7de02cf7fc 100644
--- a/configs/unet/metafile.yml
+++ b/configs/unet/metafile.yml
@@ -14,7 +14,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: DRIVE
Metrics:
mIoU: 0.680
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth
@@ -28,7 +28,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: DRIVE
Metrics:
mIoU: 0.599
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
@@ -42,7 +42,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: DRIVE
Metrics:
mIoU: 0.596
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
@@ -56,7 +56,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: STARE
Metrics:
mIoU: 0.968
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth
@@ -70,7 +70,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: STARE
Metrics:
mIoU: 0.982
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
@@ -84,7 +84,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: STARE
Metrics:
mIoU: 0.999
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
@@ -98,7 +98,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: CHASE_DB1
Metrics:
mIoU: 0.968
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth
@@ -112,7 +112,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: CHASE_DB1
Metrics:
mIoU: 0.982
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
@@ -126,7 +126,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: CHASE_DB1
Metrics:
mIoU: 0.999
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
@@ -140,7 +140,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: HRF
Metrics:
mIoU: 2.525
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth
@@ -154,7 +154,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: HRF
Metrics:
mIoU: 2.588
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
@@ -168,7 +168,7 @@ Models:
inference time (fps): 40000
Results:
- Task: Semantic Segmentation
- Dataset:
+ Dataset: HRF
Metrics:
mIoU: 2.604
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
From 0bfb6d5fa4937dd7942f22a9676a23bc0a2484de Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 24 May 2021 14:53:00 +0800
Subject: [PATCH 28/29] [Fix] Modify collection names
---
configs/unet/metafile.yml | 32 ++++++++++++--------------------
1 file changed, 12 insertions(+), 20 deletions(-)
diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml
index 7de02cf7fc..3a84cf2b2c 100644
--- a/configs/unet/metafile.yml
+++ b/configs/unet/metafile.yml
@@ -1,15 +1,7 @@
-Collections:
- - Name: UPerNet
- Metadata:
- Training Data:
- - Cityscapes
- - Pascal VOC 2012 + Aug
- - ADE20K
-
Models:
- Name: fcn_unet_s5-d16_64x64_40k_drive
- In Collection: UNet-S5-D16
+ In Collection: FCN
Metadata:
inference time (fps): 40000
Results:
@@ -23,7 +15,7 @@ Models:
- Name: pspnet_unet_s5-d16_64x64_40k_drive
- In Collection: UNet-S5-D16
+ In Collection: PSPNet
Metadata:
inference time (fps): 40000
Results:
@@ -37,7 +29,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
- In Collection: UNet-S5-D16
+ In Collection: DeepLabV3
Metadata:
inference time (fps): 40000
Results:
@@ -51,7 +43,7 @@ Models:
- Name: fcn_unet_s5-d16_128x128_40k_stare
- In Collection: UNet-S5-D16
+ In Collection: FCN
Metadata:
inference time (fps): 40000
Results:
@@ -65,7 +57,7 @@ Models:
- Name: pspnet_unet_s5-d16_128x128_40k_stare
- In Collection: UNet-S5-D16
+ In Collection: PSPNet
Metadata:
inference time (fps): 40000
Results:
@@ -79,7 +71,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
- In Collection: UNet-S5-D16
+ In Collection: DeepLabV3
Metadata:
inference time (fps): 40000
Results:
@@ -93,7 +85,7 @@ Models:
- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
- In Collection: UNet-S5-D16
+ In Collection: FCN
Metadata:
inference time (fps): 40000
Results:
@@ -107,7 +99,7 @@ Models:
- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
- In Collection: UNet-S5-D16
+ In Collection: PSPNet
Metadata:
inference time (fps): 40000
Results:
@@ -121,7 +113,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
- In Collection: UNet-S5-D16
+ In Collection: DeepLabV3
Metadata:
inference time (fps): 40000
Results:
@@ -135,7 +127,7 @@ Models:
- Name: fcn_unet_s5-d16_256x256_40k_hrf
- In Collection: UNet-S5-D16
+ In Collection: FCN
Metadata:
inference time (fps): 40000
Results:
@@ -149,7 +141,7 @@ Models:
- Name: pspnet_unet_s5-d16_256x256_40k_hrf
- In Collection: UNet-S5-D16
+ In Collection: PSPNet
Metadata:
inference time (fps): 40000
Results:
@@ -163,7 +155,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
- In Collection: UNet-S5-D16
+ In Collection: DeepLabV3
Metadata:
inference time (fps): 40000
Results:
From bcbfc1e112bc1b0528f673d2c1974aaa553f998e Mon Sep 17 00:00:00 2001
From: xiexinch <xinchen.xie@qq.com>
Date: Mon, 31 May 2021 13:31:07 +0800
Subject: [PATCH 29/29] complement inference time
---
configs/ann/metafile.yml | 14 +++++++-------
configs/apcnet/metafile.yml | 12 ++++++------
configs/ccnet/metafile.yml | 16 ++++++++--------
configs/danet/metafile.yml | 16 ++++++++--------
configs/deeplabv3/metafile.yml | 24 ++++++++++++------------
configs/deeplabv3plus/metafile.yml | 24 ++++++++++++------------
configs/dmnet/metafile.yml | 12 ++++++------
configs/dnlnet/metafile.yml | 12 ++++++------
configs/encnet/metafile.yml | 12 ++++++------
configs/fcn/metafile.yml | 22 +++++++++++-----------
configs/gcnet/metafile.yml | 16 ++++++++--------
configs/hrnet/metafile.yml | 30 +++++++++++++++---------------
configs/nonlocal_net/metafile.yml | 16 ++++++++--------
configs/ocrnet/metafile.yml | 26 +++++++++++++-------------
configs/psanet/metafile.yml | 16 ++++++++--------
configs/pspnet/metafile.yml | 22 +++++++++++-----------
configs/unet/metafile.yml | 24 ++++++++++++------------
configs/upernet/metafile.yml | 16 ++++++++--------
18 files changed, 165 insertions(+), 165 deletions(-)
diff --git a/configs/ann/metafile.yml b/configs/ann/metafile.yml
index 8ece7ee76e..17959f4282 100644
--- a/configs/ann/metafile.yml
+++ b/configs/ann/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: ann_r50-d8_512x1024_80k_cityscapes
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 3.71
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: ann_r101-d8_512x1024_80k_cityscapes
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 2.55
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: ann_r50-d8_769x769_80k_cityscapes
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 1.70
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: ann_r50-d8_512x512_160k_ade20k
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: ann_r101-d8_512x512_160k_ade20k
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 14.12
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: ann_r50-d8_512x512_40k_voc12aug
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 20.92
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: ann_r101-d8_512x512_40k_voc12aug
In Collection: ANN
Metadata:
- inference time (fps):
+ inference time (fps): 13.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/apcnet/metafile.yml b/configs/apcnet/metafile.yml
index f91635be85..de3ab01729 100644
--- a/configs/apcnet/metafile.yml
+++ b/configs/apcnet/metafile.yml
@@ -66,7 +66,7 @@ Models:
- Name: apcnet_r50-d8_512x1024_80k_cityscapes
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 3.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -80,7 +80,7 @@ Models:
- Name: apcnet_r101-d8_512x1024_80k_cityscapes
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 2.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -94,7 +94,7 @@ Models:
- Name: apcnet_r50-d8_769x769_80k_cityscapes
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -108,7 +108,7 @@ Models:
- Name: apcnet_r101-d8_769x769_80k_cityscapes
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.03
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -150,7 +150,7 @@ Models:
- Name: apcnet_r50-d8_512x512_160k_ade20k
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 19.61
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -164,7 +164,7 @@ Models:
- Name: apcnet_r101-d8_512x512_160k_ade20k
In Collection: APCNet
Metadata:
- inference time (fps):
+ inference time (fps): 13.10
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
diff --git a/configs/ccnet/metafile.yml b/configs/ccnet/metafile.yml
index 0f28967ea8..e9babb5b44 100644
--- a/configs/ccnet/metafile.yml
+++ b/configs/ccnet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: ccnet_r50-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 3.32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: ccnet_r101-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 2.31
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: ccnet_r50-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.43
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: ccnet_r101-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: ccnet_r50-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 20.89
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: ccnet_r101-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 14.11
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: ccnet_r50-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 20.45
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: ccnet_r101-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
- inference time (fps):
+ inference time (fps): 13.64
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/danet/metafile.yml b/configs/danet/metafile.yml
index a9e2b21139..233cf19a15 100644
--- a/configs/danet/metafile.yml
+++ b/configs/danet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: danet_r50-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: danet_r101-d8_512x1024_80k_cityscapes
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 1.99
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: danet_r50-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: danet_r101-d8_769x769_80k_cityscapes
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 1.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: danet_r50-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 21.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: danet_r101-d8_512x512_160k_ade20k
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 14.18
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: danet_r50-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 20.94
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: danet_r101-d8_512x512_40k_voc12aug
In Collection: DANet
Metadata:
- inference time (fps):
+ inference time (fps): 13.76
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/deeplabv3/metafile.yml b/configs/deeplabv3/metafile.yml
index 9f4da8946f..8c7e416d36 100644
--- a/configs/deeplabv3/metafile.yml
+++ b/configs/deeplabv3/metafile.yml
@@ -82,7 +82,7 @@ Models:
- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 2.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -96,7 +96,7 @@ Models:
- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 1.92
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -124,7 +124,7 @@ Models:
- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 1.11
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -138,7 +138,7 @@ Models:
- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 0.83
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -166,7 +166,7 @@ Models:
- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 6.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -292,7 +292,7 @@ Models:
- Name: deeplabv3_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 14.76
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -306,7 +306,7 @@ Models:
- Name: deeplabv3_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 10.14
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -348,7 +348,7 @@ Models:
- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -362,7 +362,7 @@ Models:
- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 9.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -390,7 +390,7 @@ Models:
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): 7.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -404,7 +404,7 @@ Models:
- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -418,7 +418,7 @@ Models:
- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
In Collection: DeepLabV3
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
diff --git a/configs/deeplabv3plus/metafile.yml b/configs/deeplabv3plus/metafile.yml
index 4d3a72af30..d5256b7894 100644
--- a/configs/deeplabv3plus/metafile.yml
+++ b/configs/deeplabv3plus/metafile.yml
@@ -82,7 +82,7 @@ Models:
- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 3.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -96,7 +96,7 @@ Models:
- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 2.60
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -124,7 +124,7 @@ Models:
- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 1.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -138,7 +138,7 @@ Models:
- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -166,7 +166,7 @@ Models:
- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 7.48
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -292,7 +292,7 @@ Models:
- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 21.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -306,7 +306,7 @@ Models:
- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 14.16
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -348,7 +348,7 @@ Models:
- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -362,7 +362,7 @@ Models:
- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -390,7 +390,7 @@ Models:
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): 9.09
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -404,7 +404,7 @@ Models:
- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -418,7 +418,7 @@ Models:
- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
In Collection: DeepLabV3+
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
diff --git a/configs/dmnet/metafile.yml b/configs/dmnet/metafile.yml
index ea7b7d070d..936b2e2d36 100644
--- a/configs/dmnet/metafile.yml
+++ b/configs/dmnet/metafile.yml
@@ -66,7 +66,7 @@ Models:
- Name: dmnet_r50-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 3.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -80,7 +80,7 @@ Models:
- Name: dmnet_r101-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 2.54
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -94,7 +94,7 @@ Models:
- Name: dmnet_r50-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.57
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -108,7 +108,7 @@ Models:
- Name: dmnet_r101-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.01
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -150,7 +150,7 @@ Models:
- Name: dmnet_r50-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 20.95
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -164,7 +164,7 @@ Models:
- Name: dmnet_r101-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
- inference time (fps):
+ inference time (fps): 13.88
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
diff --git a/configs/dnlnet/metafile.yml b/configs/dnlnet/metafile.yml
index 9a05dfbb2b..e4df52fa1c 100644
--- a/configs/dnlnet/metafile.yml
+++ b/configs/dnlnet/metafile.yml
@@ -66,7 +66,7 @@ Models:
- Name: dnl_r50-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 2.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -80,7 +80,7 @@ Models:
- Name: dnl_r101-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 1.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -94,7 +94,7 @@ Models:
- Name: dnl_r50-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 1.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -108,7 +108,7 @@ Models:
- Name: dnl_r101-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 1.02
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -150,7 +150,7 @@ Models:
- Name: dnl_r50-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 20.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -164,7 +164,7 @@ Models:
- Name: dnl_r101-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
- inference time (fps):
+ inference time (fps): 12.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
diff --git a/configs/encnet/metafile.yml b/configs/encnet/metafile.yml
index dbb8a542d8..df8bc20074 100644
--- a/configs/encnet/metafile.yml
+++ b/configs/encnet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: encnet_r50-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 4.58
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: encnet_r101-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: encnet_r50-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 1.82
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: encnet_r101-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 1.26
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: encnet_r50-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 22.81
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: encnet_r101-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
- inference time (fps):
+ inference time (fps): 14.87
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
diff --git a/configs/fcn/metafile.yml b/configs/fcn/metafile.yml
index 46e69820a5..6419a40aa4 100644
--- a/configs/fcn/metafile.yml
+++ b/configs/fcn/metafile.yml
@@ -89,7 +89,7 @@ Models:
- Name: fcn_r50-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 4.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -103,7 +103,7 @@ Models:
- Name: fcn_r101-d8_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 2.66
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -131,7 +131,7 @@ Models:
- Name: fcn_r50-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 1.80
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -145,7 +145,7 @@ Models:
- Name: fcn_r101-d8_769x769_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 1.19
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -383,7 +383,7 @@ Models:
- Name: fcn_r50-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 23.49
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -397,7 +397,7 @@ Models:
- Name: fcn_r101-d8_512x512_160k_ade20k
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 14.78
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -439,7 +439,7 @@ Models:
- Name: fcn_r50-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 23.28
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -453,7 +453,7 @@ Models:
- Name: fcn_r101-d8_512x512_40k_voc12aug
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 14.81
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -481,7 +481,7 @@ Models:
- Name: fcn_r101-d8_480x480_80k_pascal_context
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 9.93
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -495,7 +495,7 @@ Models:
- Name: fcn_r101-d8_480x480_40k_pascal_context_59
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -509,7 +509,7 @@ Models:
- Name: fcn_r101-d8_480x480_80k_pascal_context_59
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml
index 03d78931a7..c10c918a4e 100644
--- a/configs/gcnet/metafile.yml
+++ b/configs/gcnet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: gcnet_r50-d8_512x1024_80k_cityscapes
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 3.93
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: gcnet_r101-d8_512x1024_80k_cityscapes
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 2.61
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: gcnet_r50-d8_769x769_80k_cityscapes
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: gcnet_r101-d8_769x769_80k_cityscapes
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.13
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: gcnet_r50-d8_512x512_160k_ade20k
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.38
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: gcnet_r101-d8_512x512_160k_ade20k
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 15.20
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: gcnet_r50-d8_512x512_40k_voc12aug
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.35
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: gcnet_r101-d8_512x512_40k_voc12aug
In Collection: GCNet
Metadata:
- inference time (fps):
+ inference time (fps): 14.80
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml
index ed33e2d7d9..d2ac3bfa47 100644
--- a/configs/hrnet/metafile.yml
+++ b/configs/hrnet/metafile.yml
@@ -44,7 +44,7 @@ Models:
- Name: fcn_hr18s_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 23.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -58,7 +58,7 @@ Models:
- Name: fcn_hr18_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 12.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -72,7 +72,7 @@ Models:
- Name: fcn_hr48_512x1024_80k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 6.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -86,7 +86,7 @@ Models:
- Name: fcn_hr18s_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 23.74
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -100,7 +100,7 @@ Models:
- Name: fcn_hr18_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 12.97
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -114,7 +114,7 @@ Models:
- Name: fcn_hr48_512x1024_160k_cityscapes
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 6.42
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -170,7 +170,7 @@ Models:
- Name: fcn_hr18s_512x512_160k_ade20k
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 38.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -184,7 +184,7 @@ Models:
- Name: fcn_hr18_512x512_160k_ade20k
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 22.57
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -198,7 +198,7 @@ Models:
- Name: fcn_hr48_512x512_160k_ade20k
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 21.23
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -254,7 +254,7 @@ Models:
- Name: fcn_hr18s_512x512_40k_voc12aug
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 43.36
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -268,7 +268,7 @@ Models:
- Name: fcn_hr18_512x512_40k_voc12aug
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 23.48
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -282,7 +282,7 @@ Models:
- Name: fcn_hr48_512x512_40k_voc12aug
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 22.05
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -310,7 +310,7 @@ Models:
- Name: fcn_hr48_480x480_80k_pascal_context
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): 8.86
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -324,7 +324,7 @@ Models:
- Name: fcn_hr48_480x480_40k_pascal_context_59
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -338,7 +338,7 @@ Models:
- Name: fcn_hr48_480x480_80k_pascal_context
In Collection: FCN
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
diff --git a/configs/nonlocal_net/metafile.yml b/configs/nonlocal_net/metafile.yml
index 4c545ebab0..0f41ac015e 100644
--- a/configs/nonlocal_net/metafile.yml
+++ b/configs/nonlocal_net/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: nonlocal_r50-d8_512x1024_80k_cityscapes
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 2.72
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: nonlocal_r101-d8_512x1024_80k_cityscapes
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 1.95
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: nonlocal_r50-d8_769x769_80k_cityscapes
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 1.52
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: nonlocal_r101-d8_769x769_80k_cityscapes
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 1.05
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: nonlocal_r50-d8_512x512_160k_ade20k
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 21.37
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: nonlocal_r101-d8_512x512_160k_ade20k
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 13.97
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: nonlocal_r50-d8_512x512_40k_voc12aug
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 21.21
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: nonlocal_r101-d8_512x512_40k_voc12aug
In Collection: NonLocal
Metadata:
- inference time (fps):
+ inference time (fps): 14.01
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/ocrnet/metafile.yml b/configs/ocrnet/metafile.yml
index 50b6d0a5ed..fcdf72d791 100644
--- a/configs/ocrnet/metafile.yml
+++ b/configs/ocrnet/metafile.yml
@@ -53,7 +53,7 @@ Models:
- Name: ocrnet_hr18s_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 10.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -67,7 +67,7 @@ Models:
- Name: ocrnet_hr18_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 7.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: ocrnet_hr48_512x1024_80k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 4.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: ocrnet_hr18s_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 10.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: ocrnet_hr18_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 7.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -123,7 +123,7 @@ Models:
- Name: ocrnet_hr48_512x1024_160k_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 4.22
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -137,7 +137,7 @@ Models:
- Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -221,7 +221,7 @@ Models:
- Name: ocrnet_hr18s_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 28.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -235,7 +235,7 @@ Models:
- Name: ocrnet_hr18_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 18.93
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -249,7 +249,7 @@ Models:
- Name: ocrnet_hr48_512x512_160k_ade20k
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 16.99
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -305,7 +305,7 @@ Models:
- Name: ocrnet_hr18s_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 31.55
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -319,7 +319,7 @@ Models:
- Name: ocrnet_hr18_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 19.91
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -333,7 +333,7 @@ Models:
- Name: ocrnet_hr48_512x512_40k_voc12aug
In Collection: OCRNet
Metadata:
- inference time (fps):
+ inference time (fps): 17.83
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/psanet/metafile.yml b/configs/psanet/metafile.yml
index 1052ec1e19..7e2b3138ba 100644
--- a/configs/psanet/metafile.yml
+++ b/configs/psanet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: psanet_r50-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 3.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: psanet_r101-d8_512x1024_80k_cityscapes
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 2.20
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: psanet_r50-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 1.40
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: psanet_r101-d8_769x769_80k_cityscapes
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 0.98
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: psanet_r50-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 18.91
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: psanet_r101-d8_512x512_160k_ade20k
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 13.13
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: psanet_r50-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 18.24
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: psanet_r101-d8_512x512_40k_voc12aug
In Collection: PSANet
Metadata:
- inference time (fps):
+ inference time (fps): 12.63
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
diff --git a/configs/pspnet/metafile.yml b/configs/pspnet/metafile.yml
index 3823d5918a..4981f02c32 100644
--- a/configs/pspnet/metafile.yml
+++ b/configs/pspnet/metafile.yml
@@ -82,7 +82,7 @@ Models:
- Name: pspnet_r50-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 4.07
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -96,7 +96,7 @@ Models:
- Name: pspnet_r101-d8_512x1024_80k_cityscapes
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 2.68
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -124,7 +124,7 @@ Models:
- Name: pspnet_r50-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -138,7 +138,7 @@ Models:
- Name: pspnet_r101-d8_769x769_80k_cityscapes
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -264,7 +264,7 @@ Models:
- Name: pspnet_r50-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.53
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -278,7 +278,7 @@ Models:
- Name: pspnet_r101-d8_512x512_160k_ade20k
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 15.30
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -320,7 +320,7 @@ Models:
- Name: pspnet_r50-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.59
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -334,7 +334,7 @@ Models:
- Name: pspnet_r101-d8_512x512_40k_voc12aug
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 15.02
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -362,7 +362,7 @@ Models:
- Name: pspnet_r101-d8_480x480_80k_pascal_context
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): 9.68
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -376,7 +376,7 @@ Models:
- Name: pspnet_r101-d8_480x480_40k_pascal_context
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
@@ -390,7 +390,7 @@ Models:
- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
In Collection: PSPNet
Metadata:
- inference time (fps):
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: Pascal Context
diff --git a/configs/unet/metafile.yml b/configs/unet/metafile.yml
index 3a84cf2b2c..51058d00af 100644
--- a/configs/unet/metafile.yml
+++ b/configs/unet/metafile.yml
@@ -3,7 +3,7 @@ Models:
- Name: fcn_unet_s5-d16_64x64_40k_drive
In Collection: FCN
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@@ -17,7 +17,7 @@ Models:
- Name: pspnet_unet_s5-d16_64x64_40k_drive
In Collection: PSPNet
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@@ -31,7 +31,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
In Collection: DeepLabV3
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
@@ -45,7 +45,7 @@ Models:
- Name: fcn_unet_s5-d16_128x128_40k_stare
In Collection: FCN
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
@@ -59,7 +59,7 @@ Models:
- Name: pspnet_unet_s5-d16_128x128_40k_stare
In Collection: PSPNet
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
@@ -73,7 +73,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
In Collection: DeepLabV3
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: STARE
@@ -87,7 +87,7 @@ Models:
- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
In Collection: FCN
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@@ -101,7 +101,7 @@ Models:
- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
In Collection: PSPNet
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@@ -115,7 +115,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
In Collection: DeepLabV3
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
@@ -129,7 +129,7 @@ Models:
- Name: fcn_unet_s5-d16_256x256_40k_hrf
In Collection: FCN
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
@@ -143,7 +143,7 @@ Models:
- Name: pspnet_unet_s5-d16_256x256_40k_hrf
In Collection: PSPNet
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
@@ -157,7 +157,7 @@ Models:
- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
In Collection: DeepLabV3
Metadata:
- inference time (fps): 40000
+ inference time (fps): None
Results:
- Task: Semantic Segmentation
Dataset: HRF
diff --git a/configs/upernet/metafile.yml b/configs/upernet/metafile.yml
index 7c6773febc..315c25568e 100644
--- a/configs/upernet/metafile.yml
+++ b/configs/upernet/metafile.yml
@@ -67,7 +67,7 @@ Models:
- Name: upernet_r50_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 4.25
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -81,7 +81,7 @@ Models:
- Name: upernet_r101_512x1024_80k_cityscapes
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 3.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -95,7 +95,7 @@ Models:
- Name: upernet_r50_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.76
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -109,7 +109,7 @@ Models:
- Name: upernet_r101_769x769_80k_cityscapes
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 1.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
@@ -151,7 +151,7 @@ Models:
- Name: upernet_r50_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.40
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -165,7 +165,7 @@ Models:
- Name: upernet_r101_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 20.34
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
@@ -207,7 +207,7 @@ Models:
- Name: upernet_r50_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 23.17
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
@@ -221,7 +221,7 @@ Models:
- Name: upernet_r101_512x512_40k_voc12aug
In Collection: UPerNet
Metadata:
- inference time (fps):
+ inference time (fps): 19.98
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug